# Image Processing and Recognition Algorithm Design in Intelligent Imaging Device System

Authors: Xiang Li; Donggang He
Journal: Security and Communication Networks
 (2022)

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## Abstract

People may quickly employ imagig devices to acquire and use image data thanks to the rapid development of computer networks and communication technologies. However, imaging devices obtain massive data through real-time acquisition, and a large number of invalid images affect the imaging device system’s endurance on the one hand while also requiring a significant amount of time for analysis on the other hand, so there is a critical need to find a way to automate the mining of valuable information in the data. In this paper, we propose an intelligent imaging device system, which embeds a target intelligent recognition algorithm, improves the YOLOv3 model by using a method based on depth-separable convolutional blocks and inverse feature fusion structure, and finally achieves fast target detection while improving detection accuracy through the design of distance-based nonextreme suppression and loss function. By preprocessing the images and automatically identifying and saving images containing target animals, the range of the imaging device system equipment can be improved and the workload of researchers searching for target animals in images can be reduced. In this paper, we propose a method for intelligent preservation of contained target images by deploying lightweight target recognition algorithms on edge computing hardware with the intelligence of the intelligent imaging device system as the research goal. After simulation experiments, the use of this method can improve the endurance of the imaging device system and reduce the time of manual processing at a later stage.


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## Body

## 1. Introduction

As a carrier of a large amount of information, images are an important way for human beings to exchange information with the outside world. Compared with text, sound, touch, and other information sources, image information is more intuitive, vivid, reliable, and easy to understand, so the use of image technology to solve problems in human production and life has been widely concerned and researched. Since the introduction of the first electronic computer, people have gradually combined image technology with electronic computers and formed a new research field, digital image processing and analysis technology [1–3]. In addition, with the development of ultra-large-scale integration technology, the structure and performance of electronic computers have been continuously improved, and image technology has been widely used in many fields such as machine vision, intelligent transportation, agricultural engineering, and biology. After years of development, image technology has formed new application disciplines according to the characteristics of different scientific and engineering fields. Any application in any field must be predicated on the acquisition of effective image data; therefore, image imaging device systems have gradually become an important part of digital image processing and analysis technology [4, 5].Image imaging device systems rely on image sensors, so image sensors are extremely widely used in industrial production, military technology, medical devices, and consumer electronics. In recent years, microelectronics process technology has been improving, enabling image sensors to achieve higher-quality imaging in smaller sizes. In particular, with the continuous development of microprocessors, more and more microprocessor-based image sensors are appearing in different fields and moving toward high resolution, high frame rate, and high definition. In order to obtain these image data, the research of image acquisition systems is also a hot spot of concern [6]. After years of development, image acquisition systems have gained a pivotal position in military, medical, and industrial fields. With the increasing scale of integrated circuits, image acquisition systems are developing toward high resolution, high frame rate, high integration, and high reliability. However, with the in-depth research and wide application of image processing and analysis technology, relying on computers alone to complete image acquisition is no longer suitable for increasingly complex scenarios; for example, military camouflage is an indispensable tactical component of modern warfare, and the use of multispectral imaging can effectively identify camouflaged targets. In order to ensure the effectiveness of military equipment and weapon equipment testing, field tests must be conducted in different scenarios such as mountains and hills, and the images of the scene need to be captured, saved, and displayed. Most of the existing image acquisition systems rely on electronic computers and video capture cards, and not only are the acquisition systems large and costly but also they have poor imaging quality and are more sensitive to the external environment [7], making it difficult to meet the needs of efficient and convenient testing. To address the problems of current image acquisition systems, this paper develops a compact portable image acquisition device that integrates image acquisition and data storage to adapt to different application environments such as field, high altitude, and airborne. These application environments are sensitive to the size, power consumption, and reliability of the device, requiring the acquisition device to be as small as possible so that it is easy to carry and reach the site for operation, the power consumption of the device to be as low as possible to reduce energy consumption needs, the compatibility of the device to be high so that it can be applied to a variety of interfaces, and the reliability of the device to be strong so that it can work in harsh environments for a long time. Since embedded systems are used in many fields with their high performance [8–10], low power consumption, miniaturization, and high reliability and can maximize the efficiency of hardware and software, it is of wide and far-reaching practical significance to study portable image acquisition equipment based on embedded systems to realize the acquisition of image data in special scenarios. In this paper, a combination of image acquisition and embedded approach is used to provide a basis for the research of an embedded-based image acquisition system. The basic process of the imaging device system is shown in Figure 1. The light reflected from the shooting object is refracted by the lens to the image sensor CIS, which converts the light signal into an electrical signal, which is then converted to an analog signal by analog-to-digital conversion, which is then processed by DSP and transmitted to the cell phone processor, where it is finally displayed on the screen.Figure 1
Imaging flow chart.Natural scenes are the basis of human existence and development, and they are also important objects for our perception of the world. In our daily life, we have the opportunity to recognize a large number of things that are either familiar or novel on a daily basis. This task is effortless for us humans to do, even if these things have undergone some shape change, color change, or texture change [11]. But, for computers, it is often quite tricky to accurately identify a target. Since the generation of digital images, researchers have been trying to use digital image processing-related techniques to recognize targets in images or videos, and the recognition of targets in natural scenes is one of the main tasks of video surveillance systems. Feature extraction of targets is the key to target recognition. Before the 1990s, target recognition was mainly based on geometric shapes, and different shapes were used to distinguish target categories, while when the viewpoint or size changed, the category of the target was confirmed by finding the deformation relationship between geometric shapes. Obviously, there is an obvious drawback of this method; that is, when the target shape is uncertain or difficult to describe, the classification will not be completed. Moreover, this method cannot handle problems such as relatively large changes in viewpoint, lighting changes, and differentiation between targets of the same category.Understanding the precise targets that appear in a scene is a significant study problem in the video analysis work, as well as a fundamental topic for implementing an intelligent imaging device system. Computers, on the other hand, can describe and recall the target, since they cannot extract the important qualities of a target as rapidly and correctly as humans do through long-term, highly intelligent training. Therefore, computers are unable to efficiently translate high-level requirements into semantic features of images and are often unable to translate high-level semantic features into effective underlying features. Therefore, efficient video surveillance in the target recognition also faces great difficulties, of which the main technical difficulties lie in the following area: how to effectively segment the target to facilitate subsequent processing. The main reason for further segmentation of the target is that the target image usually contains a complex structure inside and therefore hides a great amount of information. When describing a person’s attire, you will frequently hear phrases like “she’s wearing a red scarf, a black shirt, and white jeans.” The simple correlation of three elements and three colors already provides a significant amount of information, allowing us to locate someone in a crowd rapidly. The various descriptions of the various elements of the image constitute a sort of advanced semantic feature extraction in and of themselves [12]. After all, utilizing merely global information like a color histogram to describe a picture appears to be very generic and nondiscriminative. Although the extraction of visually significant regions in videos or images has made great progress since the systematic computational model of visual saliency was proposed based on biological principles, most of the current algorithms still have problems such as high complexity of algorithms, inability to process in real time, and unstable results for different images in different scenes. Fast and stable significant region extraction is the basic requirement for visual saliency-based target recognition, and it is also the focus of future research. The extraction of features is the core of the target recognition problem, and good features often have an extremely strong discriminative ability, which can make the distance between similar targets very close and the difference between different categories large. At the same time, the target image in video surveillance may have problems such as rotation, scaling, being affected by lighting changes, and the presence of noise, which makes it necessary to also consider whether the extracted features can show sufficient robustness to the above-mentioned problems when making feature selection. Therefore, designing and extracting different features for different applications are an important basis for successful target recognition. In recent years, researchers have designed a variety of feature operators with excellent performance, which has greatly enhanced the efficiency of target feature extraction and promoted the progress of target recognition.Motion target detection, target categorization, target tracking, and behavior understanding are the four steps of intelligent analysis in an intelligent imaging device system, with motion target detection serving as the foundation for all other links. The effect of its separation results directly determines the quality of postprocessing such as target classification, target tracking, and behavior understanding. Motion target detection is the separation of the motion target of interest from the background image from the image sequence, and the effect of its separation results directly determines the quality of postprocessing such as target classification, target tracking, and behavior understanding. In the real environment [13–15], because the background image will produce dynamic changes such as lighting, weather, shadows, and background interference, motion target detection becomes a very difficult task; therefore, the detection of motion targets in complex scenes has become a hot spot and difficult point of research in the field of intelligent video surveillance, with important research value and practical significance. At the same time, in order to extend the duration of the video surveillance equipment, an intelligent imaging device system uses a non-real-time turn-on device strategy, usually using the “trap method” to trigger the recording; the common trap method includes infrared detectors, motion sensors, or other light sensors as a trigger organ. These technologies do not allow for targeted observation; any breeze movement could cause the recording to start. We propose an image processing and recognition algorithm in this paper for an intelligent imaging device system that intelligently identifies the studied target by deploying a lightweight target recognition algorithm on the embedded device and intelligently starts target recognition and saves when a specific target appears in front of the camera. Through simulation verification using relevant data, the method can effectively improve the system performance of the intelligent imaging device and greatly reduce the time of postscreening and identification, which has good application value.The paper’s divisional layout is as follows: The related work is presented in Section2. Section 3 analyzes the methods of the proposed work. Section 4 discusses the experimentation and results. Finally, in Section 5, the research work is concluded.


## 2. Related Work

In this section, we defined imaging device system and image processing and recognition algorithms briefly.


### 2.1. Imaging Device System

People’s desire for imaging device systems has grown in recent years, owing to the advancement of information technology, particularly the rising popularity of smartphones. A smart imaging device system is an important component for capturing images, and research into it is important not only for imaging quality control but also for consumer product selection, which is of great importance and industrial value for the development of the smart imaging device industry in China. In this paper, we use digital image acquisition and processing technology to obtain basic image quality data and apply modulation transfer function [14], chromatic aberration, dynamic range, aberration, and other parameters to design and evaluate the image processing method of intelligent imaging device system with high reliability in four aspects: sharpness, color, gradation, and geometric deformation. It provides scientific and effective solutions for downstream equipment suppliers to select imaging device systems for product design and development.The quality and reliability of the device are the top priorities in the development process of the imaging device system, from scheme design to scheme demonstration, from schematic design to device selection, and from module to system; reliability awareness should be maintained at every step and throughout the development process. The design of hardware and software should fully consider the safety and ease of use of the equipment, and, after the design is completed, the equipment should be tested comprehensively according to the test outline to ensure the safety and reliability of the equipment [15]. Therefore, the development process of the equipment should follow the reliability principle. The structure design should consider the mechanical damage of the equipment and the instability caused by mechanical impact, and the reliability of the connections should be considered. For safety principle, equipment should maintain good grounding; that is, each module of the hardware within the equipment has a good grounding, as well as hardware and mechanical structure with a good grounding; all peripheral interfaces of the equipment should have electromagnetic isolation; power supply modules should have protection functions, such as antireverse connection protection and overvoltage protection, so as to protect the safety of equipment and operators. For applicability principle, equipment in the process of use will be in different environments, especially in the harsher environment, vulnerable to transport, storage, temperature, humidity, acidity, and other factors, hence the need for higher environmental adaptability to improve the stability of the equipment, to a certain extent, to broaden the application areas of the equipment. In addition, it is also necessary to consider the anti-interference ability of the equipment and the need to take good grounding, isolation measures, and the circuit should reasonably add decoupling capacitors and bypass capacitors to ensure the adaptability of the equipment [16–19].A full camera module consists of three primary components: the lens (Lens), image sensor, and image processor. The lens module is made up of the lens (Lens), image sensor, and image processor. The main operating principles of these components are as follows.The Lens Module’s Working Principle. As shown in Figure 2, the lens and image sensor make up the lens module, which is made up of multiple components. The lens is the structure that collects light and allows the light-sensitive device to see clearly. The performance of the lens directly affects the quality of imaging and the implementation and effect of the algorithm. In a complete lens, the lens part mainly contains the lens, lens holder, and infrared filter. The most important part of the lens is the lens (Lens), based on the principle of refraction and reflection of light, as well as the convergence of light reflected from the object. At present, the industry is using compound lenses, that is, the combination of multiple concave and convex lenses, which solves the problem of poor clarity and chromatic aberration of the early single convex lens, and coating on the lens to increase the amount of incoming light; the main role of the IR filter is to filter out the light in the infrared light (some special-purpose camera modules do not need infrared filters) and infrared light for nonvisible light; the human eye cannot identify, but the image inside the module Sensor can be seen; if there is no infrared filter, the overall image will be a reddish chromatic aberration; some modules do not have an internal infrared filter but it is integrated into the image sensor surface of the microlens; The angle of view and aperture are important parameters to describe the quality of the lens [20]. When the focal length becomes longer, the perspective becomes smaller; you can make the more distant objects become clear, but the width of the range can be shot narrower. Since the lens has been manufactured, we cannot change the diameter of the lens at will, but we can add polygonal or circular, as well as variable areas of the hole grating to control the amount of light through the lens into the body; such a device is called the aperture, and it is an important parameter of the module. Aperture size is generally expressed in the F value, where the smaller the aperture F value, the larger the aperture, and so the lighter in the same unit of time.Figure 2
Imaging optical path flow chart.


### 2.2. Image Processing and Recognition Algorithms

In complex environments imaging device systems for image acquisition and processing and identification of specific targets and control can greatly reduce the occurrence of theft cases, reducing the possibility of loss and theft, and can provide the appropriate evidence. Therefore, it is important to study image processing and intelligent identification algorithm in the imaging device system. Computer vision refers to a kind of simulation of biological vision through input devices such as computers and surveillance cameras. Its main task is to process the sampled video or image to obtain information about the image target in the corresponding scene. In the image processing well recognition system, the image matching technology is very important; according to the available literature [18], the methods to deal with the matching problem can be mainly divided into the three kinds of grayscale-based matching, feature-based matching, and matching based on relational structure. Among them, the normalized grayscale intensity method is one of the most classical grayscale matching algorithms, but the drawback is also very obvious and computationally intensive. It is not generally applicable in surveillance situations. The idea is that the image is treated as a two-dimensional signal and the matching between the signals is performed using a statistical perspective. Feature-based matching is a method that obtains the geometric shape characteristics of points and lines, and surfaces in two or more images describe them parametrically, and then the described parameters are used to perform matching. Compared with grayscale-based matching methods, the number of feature points to be compared is greatly reduced compared to the pixel points of the original image, so it can greatly reduce the computational effort; at the same time, the feature point matching method makes the value of matching points more accurate, which can greatly improve the matching accuracy. The SIFT algorithm is also a classical feature-based matching algorithm. However, the drawback of this algorithm is that it is more feature-dependent, so it also has certain requirements for the target, and it can also be affected by noise or other interference factors to some extent. The relational structure-based matching method is a computer model that establishes a corresponding relational structure between external phenomena or objects and seeks to deal with the matching problem through the use of image structure, the connection between associated structural features, and the representation of the connection between objects as a structure by means of graph theory and semantic pooling. However, in the implementation of this method, the description of the structure and the relationship between points are imperfect, while lacking a scale that can accurately measure whether the points in the set are consistent with each other. Therefore, this method has not yet made a breakthrough as an application of artificial intelligence in image recognition matching technology [19]. To sum up, the most applied method is still feature-based matching. Taking face recognition and car recognition as examples, ordinary people have normal facial features, symmetrical eyes, nose, mouth, ears, and other organs with obvious geometric features, and cars can generally be seen from the side view with symmetrical tires and trapezoid-like appearance geometry, and these are the basis of features in image recognition. The same is also an important direction for the specific target recognition method in this paper. A key problem in image processing is to determine whether a set of original images contain a specific target, including its features or motion state [20]. Currently, existing techniques still find it difficult to recognize targets in arbitrary environments. However, there have been some applications in areas such as simple graphic recognition, license plate recognition, face recognition, and handwritten document recognition. However, these types of discriminations generally require a specific environment with a simple or characterized background environment and a certain requirement for the morphology of the target object in the image. Image processing techniques in applications rely heavily on image extraction input devices, and, with the widespread entry of cloud surveillance cameras into the market, a wide variety of image monitoring systems have been further popularized. This not only makes it possible to control and identify specific targets in complex environments but also makes the research content of image matching and target recognition techniques in computer vision more meaningful in practice [21].


## 2.1. Imaging Device System

People’s desire for imaging device systems has grown in recent years, owing to the advancement of information technology, particularly the rising popularity of smartphones. A smart imaging device system is an important component for capturing images, and research into it is important not only for imaging quality control but also for consumer product selection, which is of great importance and industrial value for the development of the smart imaging device industry in China. In this paper, we use digital image acquisition and processing technology to obtain basic image quality data and apply modulation transfer function [14], chromatic aberration, dynamic range, aberration, and other parameters to design and evaluate the image processing method of intelligent imaging device system with high reliability in four aspects: sharpness, color, gradation, and geometric deformation. It provides scientific and effective solutions for downstream equipment suppliers to select imaging device systems for product design and development.The quality and reliability of the device are the top priorities in the development process of the imaging device system, from scheme design to scheme demonstration, from schematic design to device selection, and from module to system; reliability awareness should be maintained at every step and throughout the development process. The design of hardware and software should fully consider the safety and ease of use of the equipment, and, after the design is completed, the equipment should be tested comprehensively according to the test outline to ensure the safety and reliability of the equipment [15]. Therefore, the development process of the equipment should follow the reliability principle. The structure design should consider the mechanical damage of the equipment and the instability caused by mechanical impact, and the reliability of the connections should be considered. For safety principle, equipment should maintain good grounding; that is, each module of the hardware within the equipment has a good grounding, as well as hardware and mechanical structure with a good grounding; all peripheral interfaces of the equipment should have electromagnetic isolation; power supply modules should have protection functions, such as antireverse connection protection and overvoltage protection, so as to protect the safety of equipment and operators. For applicability principle, equipment in the process of use will be in different environments, especially in the harsher environment, vulnerable to transport, storage, temperature, humidity, acidity, and other factors, hence the need for higher environmental adaptability to improve the stability of the equipment, to a certain extent, to broaden the application areas of the equipment. In addition, it is also necessary to consider the anti-interference ability of the equipment and the need to take good grounding, isolation measures, and the circuit should reasonably add decoupling capacitors and bypass capacitors to ensure the adaptability of the equipment [16–19].A full camera module consists of three primary components: the lens (Lens), image sensor, and image processor. The lens module is made up of the lens (Lens), image sensor, and image processor. The main operating principles of these components are as follows.The Lens Module’s Working Principle. As shown in Figure 2, the lens and image sensor make up the lens module, which is made up of multiple components. The lens is the structure that collects light and allows the light-sensitive device to see clearly. The performance of the lens directly affects the quality of imaging and the implementation and effect of the algorithm. In a complete lens, the lens part mainly contains the lens, lens holder, and infrared filter. The most important part of the lens is the lens (Lens), based on the principle of refraction and reflection of light, as well as the convergence of light reflected from the object. At present, the industry is using compound lenses, that is, the combination of multiple concave and convex lenses, which solves the problem of poor clarity and chromatic aberration of the early single convex lens, and coating on the lens to increase the amount of incoming light; the main role of the IR filter is to filter out the light in the infrared light (some special-purpose camera modules do not need infrared filters) and infrared light for nonvisible light; the human eye cannot identify, but the image inside the module Sensor can be seen; if there is no infrared filter, the overall image will be a reddish chromatic aberration; some modules do not have an internal infrared filter but it is integrated into the image sensor surface of the microlens; The angle of view and aperture are important parameters to describe the quality of the lens [20]. When the focal length becomes longer, the perspective becomes smaller; you can make the more distant objects become clear, but the width of the range can be shot narrower. Since the lens has been manufactured, we cannot change the diameter of the lens at will, but we can add polygonal or circular, as well as variable areas of the hole grating to control the amount of light through the lens into the body; such a device is called the aperture, and it is an important parameter of the module. Aperture size is generally expressed in the F value, where the smaller the aperture F value, the larger the aperture, and so the lighter in the same unit of time.Figure 2
Imaging optical path flow chart.


## 2.2. Image Processing and Recognition Algorithms

In complex environments imaging device systems for image acquisition and processing and identification of specific targets and control can greatly reduce the occurrence of theft cases, reducing the possibility of loss and theft, and can provide the appropriate evidence. Therefore, it is important to study image processing and intelligent identification algorithm in the imaging device system. Computer vision refers to a kind of simulation of biological vision through input devices such as computers and surveillance cameras. Its main task is to process the sampled video or image to obtain information about the image target in the corresponding scene. In the image processing well recognition system, the image matching technology is very important; according to the available literature [18], the methods to deal with the matching problem can be mainly divided into the three kinds of grayscale-based matching, feature-based matching, and matching based on relational structure. Among them, the normalized grayscale intensity method is one of the most classical grayscale matching algorithms, but the drawback is also very obvious and computationally intensive. It is not generally applicable in surveillance situations. The idea is that the image is treated as a two-dimensional signal and the matching between the signals is performed using a statistical perspective. Feature-based matching is a method that obtains the geometric shape characteristics of points and lines, and surfaces in two or more images describe them parametrically, and then the described parameters are used to perform matching. Compared with grayscale-based matching methods, the number of feature points to be compared is greatly reduced compared to the pixel points of the original image, so it can greatly reduce the computational effort; at the same time, the feature point matching method makes the value of matching points more accurate, which can greatly improve the matching accuracy. The SIFT algorithm is also a classical feature-based matching algorithm. However, the drawback of this algorithm is that it is more feature-dependent, so it also has certain requirements for the target, and it can also be affected by noise or other interference factors to some extent. The relational structure-based matching method is a computer model that establishes a corresponding relational structure between external phenomena or objects and seeks to deal with the matching problem through the use of image structure, the connection between associated structural features, and the representation of the connection between objects as a structure by means of graph theory and semantic pooling. However, in the implementation of this method, the description of the structure and the relationship between points are imperfect, while lacking a scale that can accurately measure whether the points in the set are consistent with each other. Therefore, this method has not yet made a breakthrough as an application of artificial intelligence in image recognition matching technology [19]. To sum up, the most applied method is still feature-based matching. Taking face recognition and car recognition as examples, ordinary people have normal facial features, symmetrical eyes, nose, mouth, ears, and other organs with obvious geometric features, and cars can generally be seen from the side view with symmetrical tires and trapezoid-like appearance geometry, and these are the basis of features in image recognition. The same is also an important direction for the specific target recognition method in this paper. A key problem in image processing is to determine whether a set of original images contain a specific target, including its features or motion state [20]. Currently, existing techniques still find it difficult to recognize targets in arbitrary environments. However, there have been some applications in areas such as simple graphic recognition, license plate recognition, face recognition, and handwritten document recognition. However, these types of discriminations generally require a specific environment with a simple or characterized background environment and a certain requirement for the morphology of the target object in the image. Image processing techniques in applications rely heavily on image extraction input devices, and, with the widespread entry of cloud surveillance cameras into the market, a wide variety of image monitoring systems have been further popularized. This not only makes it possible to control and identify specific targets in complex environments but also makes the research content of image matching and target recognition techniques in computer vision more meaningful in practice [21].


## 3. Method

In this section, we define model architecture, imaging module, image processing technology, and object detection.


### 3.1. Model Architecture

This paper proposes a set of image processing and recognition algorithms in an embedded intelligent imaging device system, which includes three basic modules from camera data input to core board processing and final display. The choice of high-end SONY CMOS devices equipped with a wide-angle lens is to expand the perspective of camera capture. In terms of software, the board is partially cut and ported for the Linux system, and the GPU programming framework is compiled by CMake based on the CUDA library, as shown in Figure3 for the software and hardware system architecture.Figure 3
System architecture diagram.


### 3.2. Imaging Module

The proposed system of imaging equipment selected a camera using Sony IMX219 sensor chip, resolution of 3280 x 2464, a field of view without a lens of 77°, aperture size of 2.0, camera focal length of 2.96 mm, and overall physical size of 25 mm × 24 mm, Previously, the traditional camera interface generally included a data bus, clock bus, and synchronization signal line control line. The physical interface block diagram is shown below. As shown in Figure4, we give a schematic diagram of the connection between the molding module and the control module.Figure 4
Common interface camera connection diagram.This camera physical interface occupies more data lines, the logic design is also more complex and needs to be strictly synchronized including a horizontal synchronization signal, vertical synchronization signal, and a clock signal, which puts forward high requirements for this end of the camera as well as the end of the receiver; at the same time, in the process of high-speed transmission, there is a direct use of digital signals as data are easily interfered by other external signals, not as stable as the differential signal, which also greatly limits the rate of transmission and the maximum image quality that the camera can transmit in real time.


### 3.3. Image Processing Technology

The characteristics of the visual system show that the human eye is more sensitive to perceiving luminance compared to color, so the correction of the light component of the unevenly illuminated images is the key to the image pre-processing optimization algorithm. Since the extraction of light components from RGB images requires simultaneous processing of three channels, which is relatively large in terms of operations, the RGB images are converted to HSI images, and only the multiscale Gaussian function is used for the extraction of light components on component I. The extracted light components are then subjected to adaptive Gamma correction, and finally the color image is synthesized and converted from HSI color space to RGB color space. The HSI color model perceives color in terms of three basic feature quantities: hue, saturation, and luminance. This design reflects the way people observe the color and is more consistent with the way people describe and interpret color and also facilitates image processing. The HSI color model is based on two important properties: first, component I is independent of the color information of the image; second, components H and S are closely linked to the way people perceive color. Because the operation of luminance component I has no effect on the other components as a result of these qualities, this work converts RGB photos to HSI images and then corrects the images with unequal lighting in the HSI color space. In order to realize the correction processing of images under a complex lighting environment, it is especially important to extract the lighting components in the images accurately. According to the Retinex theory proposed by Edwin Land, it is known that a given imageSx,y can be decomposed into two different images: the reflected object image Rx,y and the incident light image Lx,y. For each point x,y in a given image S, it can be expressed as(1)Sx,y=Rx,y⋅Lx,y.There are many methods to extract the light illumination components in images, such as the algorithm based on guided filtering, the method based on bilateral filtering, and the single-scale and multiscale Gaussian filter function methods based on Retinex theory. Given that the method using a multiscale Gaussian filter function can effectively compress the dynamic range of the image and accurately estimate the light irradiation component of the image, this paper chooses to use the multiscale Gaussian filter function to extract the light component in the image under complex lighting environment, and the Gaussian filter function used is(2)Gx,y=λ∗exp−x2+y2c2,where c is the scale factor and λ is the constant matrix, so that the Gaussian filter function Gx,y satisfies the normalization condition, that is, the spike Gx,ydxdy=1. The light component estimate of the image can be obtained by convolving the Gaussian filter function Gx,y with the luminance component Ix,y of the image, and the results are as follows:(3)Lx,y=Gx,yIx,y.According to the multiscale Retinex theory-based image enhancement method, this paper uses Gaussian filter functions of different scales to extract the light components of the image, respectively, and then weight them to finally obtain the estimated values of the light components of the image. In this paper, three Gaussian filter functions of different scales are selected to extract the light components of the image, and the weight of the extracted light components of each scale is specified as 1/3; then the extracted image light components are(4)Lx,y=∑i=1313Gix,yIx,y.


### 3.4. Object Detection

In this paper, we propose a lightweight target detection and recognition method for embedded platforms based on the YOLOv3 model, which uses depth-separable convolution instead of full convolution to reduce the overall number of model parameters and improves detection performance by using the inverse feature fusion structure; distance-based nongreat suppression method is used to optimize the regression of the bounding box and distance-based loss function of the bounding box regression is used in the model training process to improve the model training efficiency and finally applied to the embedded platform to effectively improve the detection accuracy while meeting the real-time requirements of the target detector.The lightweight network model tries to reduce the amount of model parameters and complexity while preserving model correctness, which is usually accomplished using one of two methods: network structure design or model compression. A lightweight deep learning network is conceived and implemented in this paper. The feature extraction network of this model is based on the Darknet53 network and borrowed from the MobileNetv2 backbone network for lightweight design. Among them, MobileNetv2 is a small network designed for mobile, which maintains high accuracy while greatly reducing computation through deeply separable convolutional blocks and is more friendly to hardware resources, achieving 72.0% Top-1 accuracy on the ImageNet public dataset. Behind the feature extraction network, a modified feature pyramid network (FPN) is used for feature fusion, and all 3 × 3 full convolutional layers in the network are replaced with a combination of 3 × 3 channel-by-channel convolution and 1 × 1 point-by-point convolution in order to reduce the computational effort. The improved feature pyramid network adds a reverse feature fusion structure. If the model input is a 416 × 416 3-channel visible image, the scales of the three effective feature maps input to the improved feature pyramid network after the feature extraction network are downsampled 8x, 16x, and 32x for 52 × 52, 26 × 26, and 13 × 13 size feature maps, respectively. The feature maps of the FPN are then reverse down sampled, and finally the upsampled and downsampled feature maps of each layer are fused, so that the feature maps obtained fully fuse the spatial information of the low-level feature maps and the semantic information of the high-level feature maps; that is, the fusion of multiscale feature information is enhanced, and the detection accuracy is greatly improved. The postprocessing part of the FPN network mainly consists of the detection head and the distance-based nonextreme value suppression part (DIoU-NMS). Convolution and decoding are included in the detecting head. The decoding section turns the output values of the convolutional layer into the detection frame’s anticipated values. The offset of the coordinates of the upper left and lower right corners of the detection frame from the center of the grid point and 1 foreground probability value are the last 5 values in the last dimension; that is, let the coordinates of all grid points of the feature map be. The relationship between the decoded position coordinates and the output value of the convolution layer is(5)xmin,ymin=P+expx^min,y^min+0.5,xmax,ymax=P+expx^max,y^max+0.5.The decoded detection frame position and category information predicted by the 3 detection heads are filtered by distance-based nonmaximal suppression to obtain the final detection results.The standard convolution block operation implements the joint mapping of channel correlation and spatial correlation. The deep separable convolution block is mainly composed of channel-by-channel convolution and point-by-point convolution, and the standard convolution structure is split into channel-by-channel convolution and point-by-point convolution. The theoretical assumption behind this is that the correlation and spatial correlation between channels in the convolution layer can be decoupled, and mapping them separately can better reduce the number of parameters in the network, and the schematic diagram is shown in Figure5.Figure 5
Deeply separable convolution schematic.The computation of the deep separable convolution is the sum of the computations of the channel-by-channel convolution and the point-by-point convolution.(6)ODSc=W×H×C×K×K+W×H×C×N.Therefore, the ratio of the computation of depth-separable convolution to that of conventional convolution is(7)FO=W×H×C×K×K+W×H×C×NW×H×C×K×K×N=1N+1K2.The size of the general convolution kernel is 3 × 3. Then, by using the equation, we can see that the computation is reduced nearly 9 times by using depth-separable convolution instead of standard convolution.


## 3.1. Model Architecture

This paper proposes a set of image processing and recognition algorithms in an embedded intelligent imaging device system, which includes three basic modules from camera data input to core board processing and final display. The choice of high-end SONY CMOS devices equipped with a wide-angle lens is to expand the perspective of camera capture. In terms of software, the board is partially cut and ported for the Linux system, and the GPU programming framework is compiled by CMake based on the CUDA library, as shown in Figure3 for the software and hardware system architecture.Figure 3
System architecture diagram.


## 3.2. Imaging Module

The proposed system of imaging equipment selected a camera using Sony IMX219 sensor chip, resolution of 3280 x 2464, a field of view without a lens of 77°, aperture size of 2.0, camera focal length of 2.96 mm, and overall physical size of 25 mm × 24 mm, Previously, the traditional camera interface generally included a data bus, clock bus, and synchronization signal line control line. The physical interface block diagram is shown below. As shown in Figure4, we give a schematic diagram of the connection between the molding module and the control module.Figure 4
Common interface camera connection diagram.This camera physical interface occupies more data lines, the logic design is also more complex and needs to be strictly synchronized including a horizontal synchronization signal, vertical synchronization signal, and a clock signal, which puts forward high requirements for this end of the camera as well as the end of the receiver; at the same time, in the process of high-speed transmission, there is a direct use of digital signals as data are easily interfered by other external signals, not as stable as the differential signal, which also greatly limits the rate of transmission and the maximum image quality that the camera can transmit in real time.


## 3.3. Image Processing Technology

The characteristics of the visual system show that the human eye is more sensitive to perceiving luminance compared to color, so the correction of the light component of the unevenly illuminated images is the key to the image pre-processing optimization algorithm. Since the extraction of light components from RGB images requires simultaneous processing of three channels, which is relatively large in terms of operations, the RGB images are converted to HSI images, and only the multiscale Gaussian function is used for the extraction of light components on component I. The extracted light components are then subjected to adaptive Gamma correction, and finally the color image is synthesized and converted from HSI color space to RGB color space. The HSI color model perceives color in terms of three basic feature quantities: hue, saturation, and luminance. This design reflects the way people observe the color and is more consistent with the way people describe and interpret color and also facilitates image processing. The HSI color model is based on two important properties: first, component I is independent of the color information of the image; second, components H and S are closely linked to the way people perceive color. Because the operation of luminance component I has no effect on the other components as a result of these qualities, this work converts RGB photos to HSI images and then corrects the images with unequal lighting in the HSI color space. In order to realize the correction processing of images under a complex lighting environment, it is especially important to extract the lighting components in the images accurately. According to the Retinex theory proposed by Edwin Land, it is known that a given imageSx,y can be decomposed into two different images: the reflected object image Rx,y and the incident light image Lx,y. For each point x,y in a given image S, it can be expressed as(1)Sx,y=Rx,y⋅Lx,y.There are many methods to extract the light illumination components in images, such as the algorithm based on guided filtering, the method based on bilateral filtering, and the single-scale and multiscale Gaussian filter function methods based on Retinex theory. Given that the method using a multiscale Gaussian filter function can effectively compress the dynamic range of the image and accurately estimate the light irradiation component of the image, this paper chooses to use the multiscale Gaussian filter function to extract the light component in the image under complex lighting environment, and the Gaussian filter function used is(2)Gx,y=λ∗exp−x2+y2c2,where c is the scale factor and λ is the constant matrix, so that the Gaussian filter function Gx,y satisfies the normalization condition, that is, the spike Gx,ydxdy=1. The light component estimate of the image can be obtained by convolving the Gaussian filter function Gx,y with the luminance component Ix,y of the image, and the results are as follows:(3)Lx,y=Gx,yIx,y.According to the multiscale Retinex theory-based image enhancement method, this paper uses Gaussian filter functions of different scales to extract the light components of the image, respectively, and then weight them to finally obtain the estimated values of the light components of the image. In this paper, three Gaussian filter functions of different scales are selected to extract the light components of the image, and the weight of the extracted light components of each scale is specified as 1/3; then the extracted image light components are(4)Lx,y=∑i=1313Gix,yIx,y.


## 3.4. Object Detection

In this paper, we propose a lightweight target detection and recognition method for embedded platforms based on the YOLOv3 model, which uses depth-separable convolution instead of full convolution to reduce the overall number of model parameters and improves detection performance by using the inverse feature fusion structure; distance-based nongreat suppression method is used to optimize the regression of the bounding box and distance-based loss function of the bounding box regression is used in the model training process to improve the model training efficiency and finally applied to the embedded platform to effectively improve the detection accuracy while meeting the real-time requirements of the target detector.The lightweight network model tries to reduce the amount of model parameters and complexity while preserving model correctness, which is usually accomplished using one of two methods: network structure design or model compression. A lightweight deep learning network is conceived and implemented in this paper. The feature extraction network of this model is based on the Darknet53 network and borrowed from the MobileNetv2 backbone network for lightweight design. Among them, MobileNetv2 is a small network designed for mobile, which maintains high accuracy while greatly reducing computation through deeply separable convolutional blocks and is more friendly to hardware resources, achieving 72.0% Top-1 accuracy on the ImageNet public dataset. Behind the feature extraction network, a modified feature pyramid network (FPN) is used for feature fusion, and all 3 × 3 full convolutional layers in the network are replaced with a combination of 3 × 3 channel-by-channel convolution and 1 × 1 point-by-point convolution in order to reduce the computational effort. The improved feature pyramid network adds a reverse feature fusion structure. If the model input is a 416 × 416 3-channel visible image, the scales of the three effective feature maps input to the improved feature pyramid network after the feature extraction network are downsampled 8x, 16x, and 32x for 52 × 52, 26 × 26, and 13 × 13 size feature maps, respectively. The feature maps of the FPN are then reverse down sampled, and finally the upsampled and downsampled feature maps of each layer are fused, so that the feature maps obtained fully fuse the spatial information of the low-level feature maps and the semantic information of the high-level feature maps; that is, the fusion of multiscale feature information is enhanced, and the detection accuracy is greatly improved. The postprocessing part of the FPN network mainly consists of the detection head and the distance-based nonextreme value suppression part (DIoU-NMS). Convolution and decoding are included in the detecting head. The decoding section turns the output values of the convolutional layer into the detection frame’s anticipated values. The offset of the coordinates of the upper left and lower right corners of the detection frame from the center of the grid point and 1 foreground probability value are the last 5 values in the last dimension; that is, let the coordinates of all grid points of the feature map be. The relationship between the decoded position coordinates and the output value of the convolution layer is(5)xmin,ymin=P+expx^min,y^min+0.5,xmax,ymax=P+expx^max,y^max+0.5.The decoded detection frame position and category information predicted by the 3 detection heads are filtered by distance-based nonmaximal suppression to obtain the final detection results.The standard convolution block operation implements the joint mapping of channel correlation and spatial correlation. The deep separable convolution block is mainly composed of channel-by-channel convolution and point-by-point convolution, and the standard convolution structure is split into channel-by-channel convolution and point-by-point convolution. The theoretical assumption behind this is that the correlation and spatial correlation between channels in the convolution layer can be decoupled, and mapping them separately can better reduce the number of parameters in the network, and the schematic diagram is shown in Figure5.Figure 5
Deeply separable convolution schematic.The computation of the deep separable convolution is the sum of the computations of the channel-by-channel convolution and the point-by-point convolution.(6)ODSc=W×H×C×K×K+W×H×C×N.Therefore, the ratio of the computation of depth-separable convolution to that of conventional convolution is(7)FO=W×H×C×K×K+W×H×C×NW×H×C×K×K×N=1N+1K2.The size of the general convolution kernel is 3 × 3. Then, by using the equation, we can see that the computation is reduced nearly 9 times by using depth-separable convolution instead of standard convolution.


## 4. Experiment and Results

In this part, we define experimental environment, dataset, and performance comparison in detail.


### 4.1. Experimental Environment

The experimental analysis process of this model is divided into the model training process and the actual performance testing process. Among them, the model training process is completed in the server, and the deep learning framework is PyTorch; the actual performance testing environment of the model is Jetson AGX Xavier, an embedded GPU device from NVIDIA, and the specific hardware platform configuration information is shown in Table1.Table 1
Hardware platform configuration table.
Hardware nameModelConfigurationCPUIntel i7-9700KEight cores and eight threads, 3.6 GHzGPUNVIDIA GEFORCE RTX2060∗1—Embedded deviceJetson AGX XavierRunning memory 32GB


### 4.2. Dataset

The model is trained and tested based on the VOC2007 + 2012 dataset, and a class of trucks (trucks) are added to the original 20 classes of targets and labeled to cope with the distinction of specific classes in real scenes challenge. Many excellent computer vision models such as classification, localization, detection, segmentation, and action recognition are based on the PASCALVOC Challenge and its dataset, especially some classical target detection models. For the augmented VOC dataset, a mean clustering algorithm is needed to obtain the size parameters of the prior frame, which helps to reduce the offset of the prior frame and the predicted bounding box and improve the detection rate. The process of mean clustering can be described as follows: select a sample as the cluster center; calculate the distance between each sample and each cluster center; return each sample to the cluster center with the closest distance; find the mean value of the samples of each class as the new cluster center; determine that the cluster center no longer changes or reaches the number of iterations, and the algorithm ends; otherwise, go back to the second step. In addition, the model training adopts a migration learning method, in which the pretrained feature extraction network is migrated to obtain the already trained network parameters and loaded onto the target detection network so that it has the ability to recognize the underlying common features, which saves the training time and reduces the risk of underfitting and overfitting to a certain extent.


### 4.3. Performance Comparison

Table2 shows the comparison between this model and the classical deep learning target detection algorithm. The experimental results show that, compared with the benchmark model YOLOv3, replacing the backbone network and introducing depth-separable convolution reduce the overall number of model parameters nearly 6 times and improve the detection speed by 34 FPS compared with YOLOv3; using the inverse feature fusion structure and distance-based nonextreme suppression method, the model detection accuracy is improved by 2% without introducing too many parameters. The detection accuracy of the model is improved by 2% without introducing too many parameters. Overall, in terms of detection accuracy, the mAP of this model reaches 80.22%, which is the highest compared with other models. In terms of detection speed and number of model parameters, YOLOv3-Tiny performs best, but the accuracy rate is 15% different from the mAP of this model, which largely causes missed and false detection of the target. Overall, this algorithm achieves good results in terms of detection accuracy and real-time detection.Table 2
Comparison of different model algorithms.
ModelBackboneSizemAP/%FPSNumber of references/MFaster RCNNResNet50—79.5110137.1SSDVGG16300 × 30076.804733.1YOLOv3Darknet53416 × 41678.254561.6YOLOv3-Tiny—416 × 41665.241915.9OursMobileDarknet416 × 41680.227910.8Table3 shows the comparison of the detection accuracy of several different categories; from the table, we can see that the detection accuracy of the model for the categories of person and car is the same as that of the benchmark model (YOLOv3), and the detection accuracies in other categories are higher than that in the benchmark model by 0.03∼0.06. The detection accuracy of the model reaches 85% and above in different categories shown in the table, showing that the present model still has good detection results for different types of targets.Table 3
Comparison of detection accuracy of different categories.
ModelBackboneSizeAP-carAP-personAP-busAP-truckAP-aeroplaneFaster RCNNResNet50—0.880.860.850.830.83SSDVGG16300 × 3000.840.790.860.870.77YOLOv3Darknet53416 × 4160.890.850.830.830.84YOLOv3-Tiny—416 × 4160.710.700.810.790.62OursMobileDarknet416 × 4160.890.850.860.890.87


## 4.1. Experimental Environment

The experimental analysis process of this model is divided into the model training process and the actual performance testing process. Among them, the model training process is completed in the server, and the deep learning framework is PyTorch; the actual performance testing environment of the model is Jetson AGX Xavier, an embedded GPU device from NVIDIA, and the specific hardware platform configuration information is shown in Table1.Table 1
Hardware platform configuration table.
Hardware nameModelConfigurationCPUIntel i7-9700KEight cores and eight threads, 3.6 GHzGPUNVIDIA GEFORCE RTX2060∗1—Embedded deviceJetson AGX XavierRunning memory 32GB


## 4.2. Dataset

The model is trained and tested based on the VOC2007 + 2012 dataset, and a class of trucks (trucks) are added to the original 20 classes of targets and labeled to cope with the distinction of specific classes in real scenes challenge. Many excellent computer vision models such as classification, localization, detection, segmentation, and action recognition are based on the PASCALVOC Challenge and its dataset, especially some classical target detection models. For the augmented VOC dataset, a mean clustering algorithm is needed to obtain the size parameters of the prior frame, which helps to reduce the offset of the prior frame and the predicted bounding box and improve the detection rate. The process of mean clustering can be described as follows: select a sample as the cluster center; calculate the distance between each sample and each cluster center; return each sample to the cluster center with the closest distance; find the mean value of the samples of each class as the new cluster center; determine that the cluster center no longer changes or reaches the number of iterations, and the algorithm ends; otherwise, go back to the second step. In addition, the model training adopts a migration learning method, in which the pretrained feature extraction network is migrated to obtain the already trained network parameters and loaded onto the target detection network so that it has the ability to recognize the underlying common features, which saves the training time and reduces the risk of underfitting and overfitting to a certain extent.


## 4.3. Performance Comparison

Table2 shows the comparison between this model and the classical deep learning target detection algorithm. The experimental results show that, compared with the benchmark model YOLOv3, replacing the backbone network and introducing depth-separable convolution reduce the overall number of model parameters nearly 6 times and improve the detection speed by 34 FPS compared with YOLOv3; using the inverse feature fusion structure and distance-based nonextreme suppression method, the model detection accuracy is improved by 2% without introducing too many parameters. The detection accuracy of the model is improved by 2% without introducing too many parameters. Overall, in terms of detection accuracy, the mAP of this model reaches 80.22%, which is the highest compared with other models. In terms of detection speed and number of model parameters, YOLOv3-Tiny performs best, but the accuracy rate is 15% different from the mAP of this model, which largely causes missed and false detection of the target. Overall, this algorithm achieves good results in terms of detection accuracy and real-time detection.Table 2
Comparison of different model algorithms.
ModelBackboneSizemAP/%FPSNumber of references/MFaster RCNNResNet50—79.5110137.1SSDVGG16300 × 30076.804733.1YOLOv3Darknet53416 × 41678.254561.6YOLOv3-Tiny—416 × 41665.241915.9OursMobileDarknet416 × 41680.227910.8Table3 shows the comparison of the detection accuracy of several different categories; from the table, we can see that the detection accuracy of the model for the categories of person and car is the same as that of the benchmark model (YOLOv3), and the detection accuracies in other categories are higher than that in the benchmark model by 0.03∼0.06. The detection accuracy of the model reaches 85% and above in different categories shown in the table, showing that the present model still has good detection results for different types of targets.Table 3
Comparison of detection accuracy of different categories.
ModelBackboneSizeAP-carAP-personAP-busAP-truckAP-aeroplaneFaster RCNNResNet50—0.880.860.850.830.83SSDVGG16300 × 3000.840.790.860.870.77YOLOv3Darknet53416 × 4160.890.850.830.830.84YOLOv3-Tiny—416 × 4160.710.700.810.790.62OursMobileDarknet416 × 4160.890.850.860.890.87


## 5. Conclusion

Embedding target detection algorithms in intelligent imaging device systems has been a popular direction for computer vision development in recent years and it has been applied more successfully in many fields. However, as society evolves and technology advances, technology must also evolve and be updated to suit the needs of various scenarios, such as practical scenarios, which frequently necessitate detection models that consider both real-time detection and accuracy. This study develops a lightweight image processing and target detection and recognition model to provide fast and accurate detection and identification results in order to meet the application requirements of the intelligent imaging device system. The model designed in this paper is based on the architecture of YOLOv3, and the model is improved by using a method based on depth-separable convolutional blocks and inverse feature fusion structure, and detection frame screening is performed by distance-based nongreat suppression to finally obtain a more accurate detection results, and the boundary frame loss function used in the training process is DIOU-Loss, which improves the training effect of the model. The model is compared to the classical target detection model using the public target detection dataset VOC as a benchmark, and the experimental results show that the detection speed and accuracy have improved. Finally, the model was put through its paces on the intelligent imaging device system platform, where it demonstrated high robustness and generalization ability in a variety of scenarios using the project’s internal dataset.



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*Source: 9669903-2022-05-19.xml*