# Application of Emotional Factors of Ink Symbols Evaluated by Network Model in Modern Visual Image Design

Authors: Xiaoyan Yang; Le Fan; Weiwei Wang; Xiaojie Yang
Journal: Wireless Communications and Mobile Computing
 (2022)

---

## Abstract

Accurate and fast recognition of ink symbols can enhance the perception ability of intelligent ink symbols in the environment, provide information input for the operation of intelligent ink symbols, and improve the perfection of ink painting. This paper proposes an end-to-end deep neural network model based on YOLOv3 for overall and individual recognition of ink symbols from a computer vision perspective. Ink symbol images generated by the simulation software are used for learning the overall and individual ink symbol detection models. Experiments demonstrate that the YOLOv3 detection algorithm has a good detection effect on ink symbolic targets, and the recognition of individual ink symbols has higher accuracy and flexibility, which provides a preliminary solution idea to solve the ink symbol information perception problem.


---

## Body

## 1. Introduction

Today, in an era of rapid cultural interaction, exchange, and integration, we must actively explore our own traditional culture and create a national design firm with Chinese characteristics to promote the trend of Chinese traditional culture design [1]. As we all know, ink symbols have always played a very important role in traditional Chinese culture and are one of the most distinctive art styles with national characteristics, which contain the cultural connotation and essence of the traditional Chinese nation [2]. Therefore, we have the responsibility and obligation to continue to dig deeper into this cultural style of ink and wash and create more perfect artwork that meets the trend of the time. The combination of ink symbols and modern visual image design should explore the spiritual connotation and expression of ink symbols and modern graphic images through the combination of transferring and transforming the emotional factors of ink and wash [3]. This will also be a new aesthetic breakthrough. Using contemporary design techniques to view and analyze the emotional role of ink symbols on the transmission of modern visual images design is bound to be a profound research direction.Ink symbols have always played an important role in the development of traditional Chinese culture [4]. Modern Chinese visual image design thinking can make full use of the emotion of ink symbols to develop designers' design inspiration and gradually make the modern visual image design work of national and contemporary design thinking coincide with the design more strongly. The artistic expression is more vivid and emotionally intense compared with the general design of ink work, making the form of traditional ink symbols significantly sublimated [5, 6].Visual images, like words, are the most basic form of art language for human beings to understand things, and they are texts that disseminate information and record the marks of social progress for the masses of working people [7]. The study of the emotional elements of ink symbols in modern visual image design is still in the exploratory stage in the field of contemporary design. Therefore, various forms of ink symbolic emotional experimentation on visual graphic images have been carried out and are in need of continuous artistic creation by designers [8]. Concise, beautiful, generous, and even diversified forms of art design styles break the traditional forms of decorative art, so that contemporary ink symbols and modern visual graphic functions are continuously integrated to explore their spiritual connotations.This paper intends to deconstruct and reorganize the development trend of the emotional factors of ink symbols in modern visual graphic design under the background of the great cultural prosperity and development of modern society and to study the art form and artistic emotional expression of ink symbols by combining the emotional characteristics of contemporary visual law, reasoning, and expression of ink symbols, analyzing the traditional cultural heritage and design thinking inherent in ink symbols and gradually [9]. We also explore how the emotional factors of ink symbols and their art forms can contribute to the development direction of modern visual image design in China, influence people's visual judgment, and ultimately combine the emotional factors of ink symbols with modern visual image design to move the cause of contemporary ink design in China forward [10].There is still a gap in domestic and international research on the recognition of ink symbols using computer vision. This paper proposes an end-to-end deep neural network model based on YOLOv3 for overall and individual recognition of ink symbols from a computer vision perspective [11, 12]. Ink symbol images generated by the simulation software are used for learning the overall and individual ink symbol detection models. Experiments show that the YOLOv3 detection algorithm has a good detection effect on ink symbolic targets, and the recognition of individual ink symbols has higher accuracy and flexibility, which provides a preliminary solution to the problem of ink symbol information perception.


## 2. Related Work

As a new design element, ink symbols have played a pivotal role in the design history of Japan. Korea and other Asian countries, as Japanese and Korean societies, have advanced to the information stage in designer fashions. Japanese ink and Korean ink expressions are completely independent forms of design in the history of painting and design in their own countries, and they occupy an important position in the development of the diversity of their cultures. From the perspective of the development and cultural integration of Japanese ink and wash [13], Japanese ink and wash is, to a certain extent, a style of ink and wash that is in line with the national characteristics of the country after constantly borrowing and extracting the results of Chinese ink and wash symbols. Korean ink and wash are more diversified than Chinese ink and wash, but their cultural connotation is still to be investigated because their cultural heritage is not as strong as that of China.From foreign studies, Japanese ink elements are an artistic style that focuses on new type design to convey the national language, while Korean research on ink focuses on the expression of ink and tool skills, using computer brush tools to integrate ink to enrich the wooliness of the ink brush to make the design work more forward-looking [14]. Therefore, it can be seen that the designers have not yet formed a complete and differentiable theoretical system for the study of the emotional factor of ink symbols in modern multidimensional visual image design.With the continuous development of social and economic globalization, it has developed into a period of diversified cultural changes. Contemporary designers should be awake to the fact that traditional ink and wash symbols will also usher in a new situation of great cultural exchange and cultural integration, which requires our creators to continuously explore the development of traditional ink and wash symbols on the basis of new technologies to create a new form of artistic expression that meets the development of contemporary society and can be accepted by the majority of audiences [15]. With the help of advanced science and technology, and well-designed software means to better integrate traditional Chinese ink and wash symbols into the trend of social and cultural change, which will help traditional ink and wash symbols to create more permanent artworks that conform to the mainstream values of society with the help of modern science and technology.In the ink practice class, Mr. Li Ning once said that tradition is the old order relationship, and the old order relationship will be demonstrated by the new order relationship [16]. Breakthrough the rules of symbols in the traditional sense and structure them innovatively to gradually form a unique form of the art design. As (Figure 1) contemporary ink design works perfectly combining ink dots and modern visual images to create design works reflecting contemporary information, we will find that, as society develops, people's pursuit of beauty becomes more and more profound and demanding, making more and more ink symbolic art creation more and more necessary to keep up with the times [17]. In modern visual image design, designers often use diverse forms of artistic expression to innovate in content and form and make bold breakthroughs to make the emotion of ink symbols contemporary and groundbreaking in modern visual image design, which also gives people a more profound and comprehensive artistic experience and spiritual understanding of traditional Chinese art [18, 19]. In order to make design works more vivid and closer to life and reflect people's life, we must put the understanding and following of art laws in the first place of design works, try to combine these individual characteristics with art laws effectively, and make full use of our own cultivation and cultural heritage accumulation to draw excellent design thinking and design concepts to design artworks that follow the trend of the great cultural development and prosperity of Dangdu [20].Figure 1
Contemporary ink symbol works combined with modern science and technology.Today's society is a pluralistic society, and the progress of science and technology will lead to a richer development of the emotion of ink symbols in modern visual image design. In the creation of the subject matter to expand the rich imagination and create the ability to make the abstract ink art form into an easily accepted cultural form, the emotion of ink symbols using advanced science and technology will then combine with the imagination of the designer to certainly create the perfect design work [21]. The unique characteristic performance of ink symbols makes them permeable and random in art creation, while the requirements of modern visual image design are very strict [22].In the process of design creation practice, modern high technology in the form of traditional art expression should have a mastery of the use of emotional factors in ink symbols to avoid the development of science and technology to make the cultural characteristics of ink symbols with the development of science and technology and lose their unique spiritual and cultural connotation [23].Traditional ink symbols are often presented visually using a two-dimensional approach to the image, which often appears to be visually and aesthetically weak to people. The emotional expression of ink symbols relies on modern high-tech culture as the basis for continuous exploration, using two-dimensional images and H-dimensional space to show the visual effect of forming ink movement [24]. This will certainly promote the emotion of modern ink symbols to have more development paths and will certainly play to their unique cultural and contemporary value better in the future design paths.The continuous innovation of artistic expression forms should be carried out on the basis of their critical inheritance, and only by looking at traditional culture from a dialectical perspective of W, can we maintain its topicality and vitality. In modern visual image design, creators should strive to make ink symbols use multidimensional art styles to convey new cultural and aesthetic information [25]. To a certain extent, the connotation of ink symbols can w show the traditional Chinese philosophical theories, such as (Figure 2) contemporary ink design works, using abstract ink symbols emotions to express the meaning of life, which requires us to create art only when the traditional aesthetic concept, artistic expression, and modern art concept integration and give multidimensional visual images a new artistic expression, in order to better display the ink symbols The cultural essence of ink symbols can only be better displayed by integrating traditional aesthetic concepts and artistic expressions with modern artistic concepts and giving new artistic expression to multidimensional visual images. In order to make the traditional ink symbols in modern visual image design have a broader direction and more flexible forms of expression so that they can continue to survive in different design environments and continuously promote China's ink culture and art form to the world's contemporary design culture trend.Figure 2
Ink and water creative poster.


## 3. Ink Symbol Recognition Algorithm Based on YOLOv3 Deep Neural Network

YOLO, the first end-to-end detection method based on deep learning, eliminates the candidate frame extraction branch and uses the full graph as the input to the network, treating the target detection as a single regression problem.


### 3.1. Prediction Vector

The YOLO model uses a convolutional neural network structure, the beginning convolutional layer extracts the image features, the final fully connected layer predicts the output probability, and the final outputS∗S∗Β∗x+y+w+h+confidence+C tensor gives the predicted object’s enclosing frame location, size, category, and probability of belonging to that category [26].The detection principle of the YOLO model is as follows: first, the model divides the input image intoS∗S grids, and if the center of an object falls in the grid, then this grid is responsible for predicting that object. Each grid has to predict the probability of B enclosure and C categories PrClassi|Object, and each grid predicts only one category. Each bracket contains five prediction values: the coordinates x and y of the center of the bracket with respect to the center of the grid, the ratio w and h of the width and height of the bracket with respect to the width and height of the image, and the object confidence score confidence [27]. The probability of predicting a category for each bracket is(1)PrClassi|Object∗PrObject∗IOUpredtruth=PrClassi∗IOUpredtruth.This category probability contains the probability of the category condition and the accuracy of the enclosing frame position. Finally, a threshold is set to compare with this probability, and the enclosing frames with scores below the threshold are filtered out, and those with scores above the threshold are suppressed by nonmaximal values to obtain the final detected frames [28].YOLOv3 uses a newly designed Darknet53 residual network combined with a multiple prediction (FPN) network structure, in which the second two feature maps are sampled and aggregated with the corresponding size of the first two feature maps of the network, and then the prediction results are obtained after the convolutional network, which improves the detection speed of YOLOv3 while maintaining better detection accuracy.


### 3.2. Loss Function

In order to achieve the best prediction, a function is needed to measure the error between the predicted and true values of the model and to minimize this function for optimization purposes, which is the loss function. YOLO optimizes all terms of the loss function uniformly with mean square and error, and its design considers the following three main aspects:(1)
Position prediction error of the envelopeλcoord∑i=0l.h∗l.w∑j=0ln1ijobjxi−x^l2+yi−y^l2 and size prediction error λcoord∑i=0l.h∗l.w∑j=0ln1ijobjwi−w^l2+hi−h^l2(2)
The prediction error of the object confidence score of the enclosing frame, consisting of the prediction error of the confidence score of the enclosing frame without objectλnoobj∑i=0l.h∗l.w∑j=0l.n1ijnoobjCi−C^l2 and the prediction error of the confidence score of the enclosing frame with the object λobj∑i=0l.h∗l.w∑j=0l.n1ijobjCi−C^l2;\(3)
Class prediction errorλclass∑i=0l.h∗l.w∑iobj∑c∈classespic−p^lc2, of which xi,yi,wi,hi,ci,pi is the network prediction and x^i,y^i,w^i,h^i,c^i,p^i is the labeled valueThe flow of ink symbol target recognition network training is shown in Figure3. Ink symbol recognition is divided into overall recognition and individual recognition. In overall recognition, the ink symbols displayed in the front view, side view (left and right), and tail view of each ink painting are taken as a whole, and the type and state of the target ink painting, as well as the orientation interval of other ink paintings relative to mine, can be directly obtained based on the recognition results [29]. The single recognition process takes four colors of ink symbols, red, green, white, and yellow, as recognition targets.Figure 3
Schematic diagram of ink symbol target recognition based on YOLOv3.


## 3.1. Prediction Vector

The YOLO model uses a convolutional neural network structure, the beginning convolutional layer extracts the image features, the final fully connected layer predicts the output probability, and the final outputS∗S∗Β∗x+y+w+h+confidence+C tensor gives the predicted object’s enclosing frame location, size, category, and probability of belonging to that category [26].The detection principle of the YOLO model is as follows: first, the model divides the input image intoS∗S grids, and if the center of an object falls in the grid, then this grid is responsible for predicting that object. Each grid has to predict the probability of B enclosure and C categories PrClassi|Object, and each grid predicts only one category. Each bracket contains five prediction values: the coordinates x and y of the center of the bracket with respect to the center of the grid, the ratio w and h of the width and height of the bracket with respect to the width and height of the image, and the object confidence score confidence [27]. The probability of predicting a category for each bracket is(1)PrClassi|Object∗PrObject∗IOUpredtruth=PrClassi∗IOUpredtruth.This category probability contains the probability of the category condition and the accuracy of the enclosing frame position. Finally, a threshold is set to compare with this probability, and the enclosing frames with scores below the threshold are filtered out, and those with scores above the threshold are suppressed by nonmaximal values to obtain the final detected frames [28].YOLOv3 uses a newly designed Darknet53 residual network combined with a multiple prediction (FPN) network structure, in which the second two feature maps are sampled and aggregated with the corresponding size of the first two feature maps of the network, and then the prediction results are obtained after the convolutional network, which improves the detection speed of YOLOv3 while maintaining better detection accuracy.


## 3.2. Loss Function

In order to achieve the best prediction, a function is needed to measure the error between the predicted and true values of the model and to minimize this function for optimization purposes, which is the loss function. YOLO optimizes all terms of the loss function uniformly with mean square and error, and its design considers the following three main aspects:(1)
Position prediction error of the envelopeλcoord∑i=0l.h∗l.w∑j=0ln1ijobjxi−x^l2+yi−y^l2 and size prediction error λcoord∑i=0l.h∗l.w∑j=0ln1ijobjwi−w^l2+hi−h^l2(2)
The prediction error of the object confidence score of the enclosing frame, consisting of the prediction error of the confidence score of the enclosing frame without objectλnoobj∑i=0l.h∗l.w∑j=0l.n1ijnoobjCi−C^l2 and the prediction error of the confidence score of the enclosing frame with the object λobj∑i=0l.h∗l.w∑j=0l.n1ijobjCi−C^l2;\(3)
Class prediction errorλclass∑i=0l.h∗l.w∑iobj∑c∈classespic−p^lc2, of which xi,yi,wi,hi,ci,pi is the network prediction and x^i,y^i,w^i,h^i,c^i,p^i is the labeled valueThe flow of ink symbol target recognition network training is shown in Figure3. Ink symbol recognition is divided into overall recognition and individual recognition. In overall recognition, the ink symbols displayed in the front view, side view (left and right), and tail view of each ink painting are taken as a whole, and the type and state of the target ink painting, as well as the orientation interval of other ink paintings relative to mine, can be directly obtained based on the recognition results [29]. The single recognition process takes four colors of ink symbols, red, green, white, and yellow, as recognition targets.Figure 3
Schematic diagram of ink symbol target recognition based on YOLOv3.


## 4. Algorithm Testing and Result Analysis

### 4.1. Experimental Platform

The experimental environment for this paper is Intel (R) Core i5-4590 CPU @3.30 GHz∗ 4, 3.6 Gb internal memory, NVIDIA GeForce GTX980, Ubuntu 16.04, 64 bit OS [30].


### 4.2. Dataset Production

Since it is difficult to obtain the actual ink symbol images, this paper will use the images generated by the ink symbolic simulation software as the main data set for the training of the model. There are 71 different types of ink symbols, and there are 44 different ink symbol states because some of them show the same ink symbolic state. In the ink symbolic simulation software, the images of ink symbols in these 44 states were intercepted, and 500 images were obtained as data set 1. As shown in Table1, individual ink symbols were divided into four categories by color as the detection target, and the data set was labeled and used as the training set [31].Table 1
Target data for the two datasets.
Data setTypeQuantityResolving powereData set1Whitelight1210416∗ 416Data set1Redlight407416∗ 416Data set1Greenlight211416∗ 416Data set1Yellowlight158416∗ 416Data set2navLlight451416∗ 416Data set2navRlight449416∗ 416To test whether the network model obtained by deep learning using simulated ink symbolic images can detect the ink symbols in the actual captured images, as well as directly use the ink symbols as a whole as a detection target. The side view is divided into two kinds, in this paper, the target is located on my left or right side, and the two kinds of targets are recorded as navLlight and navRlight, and the dataset is labeled and divided into the training set and test set in the ratio of 10 : 1.


### 4.3. Experiment and Result Analysis

In this paper, we use the Darknet framework with pretrained model structure tiny-YOLOv3, and pretrained model parameter weight YOLOv3-tiny.Cont.15 to train and detect the overall ink symbols and individual ink symbols in turn for the experiments. In the preprocessing stage, the three parameters, saturation, exposure, and hue, are set for data enhancement.In order to objectively evaluate the effect of detection and recognition, three evaluation indicators are used here, namely, the accuracy rateCr, the false detection rate Fr, and the missed detection rate Lr. Suppose the total number of ink symbolic targets in the detection data is N, and the number of correctly detected ink symbols is Nc. The number of false detections is Nf, and the number of missed detections is Nl [8]. Then, the accuracy rate is calculated as(2)Cr=NcN.The false detection rate is calculated by the formula(3)Fr=NfNf+N.The formula for calculating the leakage rate is(4)Lr=NlN.The training set of dataset 2 is trained, and the training is stopped after 18 hours and 40,000 iterations under GPU acceleration to obtain the target detection model.First, the test set of dataset 2 was used for testing, and some of the results were rendered for comparison as shown in Figure4. Then, the side view image of the in-air dragging ink painting ink symbols with an ink painting length longer than or equal to 50 m and a dragging length greater than 200 m was selected for testing because this group of ink symbols and the ink symbols of the dataset 2 have high similarity, and its ink symbols contain the ink symbol features of dataset 2, and the detection results are shown in Figure 4. Finally, the actual shot with the highest quality in the side view image of the ink painting is selected for testing, and the detection results are shown in Figure 5. According to the detection results in Figure 4, the network model cannot correctly recognize the ink symbolic images that contain other ink symbols besides navRlight and navLlight and will misdetect them as navRlight and navLlight. Without adding negative samples in this training, the model will recognize the other ink symbols that appear as background and will not determine them as a whole.Figure 4
Comparison of the overall rendering results of the ink symbols of the simulated image.Figure 5
The overall inspection result of the actual side view of the normally drawn ink painting.In the actual ink painting detection, the left image correctly detects that the ink symbol is located on the left side, but the reflection of the ink symbol on the water surface is mistakenly detected as part of the ink symbol, which also appears in the detection frame. The right image has a large number of bright clouds in the sky, which interferes with the model, and the ink symbol is a very small target relative to the whole image and is not detected [32].It can be seen that the ink symbol as a whole is a detection target, a one-time realization of the detection of ink symbols and the type of ink symbols represented by them to discriminate the existence of greater difficulty. The most important reason is the variety of ink symbols. The actual situation as needed will be the presence of other lighting equipment and lights, which can cause interference with the correct detection of ink symbols. Secondly, due to some ink symbols of ink symbols in some angles, there is a high degree of similarity. For example, in the mooring in the execution of a pilotage task, ink painting side view and normal ink painting of the left side view, the number and color of ink symbols are the same, only the relative position between the lights is different. These characteristics are not learned by the model. Finally, the water reflection, sky clouds, and other background noise with relatively large brightness also affect the correct recognition of the ink symbols [33].


### 4.4. Training and Detection of Single Ink Symbols

Training is performed on dataset 1, and the model is iterated 500000 times to reach the optimum model, and the target detection model is obtained. The test set of dataset 1 is tested, and some results are shown in Figure6. The actual scene image is tested as shown in Figure 7. In the clean background scene of Figure 6, the foremast light, mizzen light, and outboard light are correctly identified, no false detection and no missed detection. But the partial reflection of the ink symbol on the water surface and a nonink symbol light fixture in the figure are mistakenly detected as ink symbols, and the shore light in the figure is mistakenly detected as ink symbols.Figure 6
Single ink symbol target detection conversion results.Figure 7
Ink painting actual image style conversion results.For the scene where ink symbols are reflected on the water surface in Figure7, the shape and size features of the water surface reflection are used to reject them, i.e., image preprocessing. The original input image is transformed into a grayscale image, and after the threshold operation using the Otsu method [13], the contour detection is performed, and the detected contour is judged by the roundness and external rectangle area. The detection effect after the completion of the preprocessing operation is shown in Figure 8.Figure 8
Image preprocessing detection effect in water reflection scene.Collected maritime ink symbolic images were preprocessed and then detected, and the statistical results are shown in Table2. In the detection of the actual captured ink symbol images, the accuracy of the individual ink symbol detection method reaches 63.79%, which is much higher than the overall ink symbol detection algorithm, and the false detection rate and leakage rate are lower than the overall ink symbol detection methods. The main reason for the high miss rate of the single ink symbol target detection algorithm is that the ink symbols in some scenes are only a few pixels in size, which is a very small target in a 416∗416 image, and the detection difficulty is high.Table 2
Comparison of experimental results.
Experimental objectNumber of iterationsTraining timeDetection typeNumber of testsAccuracy (%)False detection rate (%)Missed detection rate (%)Number of recognized images per secondIntegral light4000018 hSimulation1471.4322.220.080.0Actual333.035.7190.9135.59Single signal lamp500000222HSimulation8381.936.7410.8410.68Actual5863.793.3332.7697.19The test results show that the deep learning method based on YOLOv3 can be applied to the recognition and detection of ink symbols at sea, and the recognition of individual ink symbols can improve the detection accuracy, which can more flexibly cope with the detection of ink symbols under different ink symbol types and states.


## 4.1. Experimental Platform

The experimental environment for this paper is Intel (R) Core i5-4590 CPU @3.30 GHz∗ 4, 3.6 Gb internal memory, NVIDIA GeForce GTX980, Ubuntu 16.04, 64 bit OS [30].


## 4.2. Dataset Production

Since it is difficult to obtain the actual ink symbol images, this paper will use the images generated by the ink symbolic simulation software as the main data set for the training of the model. There are 71 different types of ink symbols, and there are 44 different ink symbol states because some of them show the same ink symbolic state. In the ink symbolic simulation software, the images of ink symbols in these 44 states were intercepted, and 500 images were obtained as data set 1. As shown in Table1, individual ink symbols were divided into four categories by color as the detection target, and the data set was labeled and used as the training set [31].Table 1
Target data for the two datasets.
Data setTypeQuantityResolving powereData set1Whitelight1210416∗ 416Data set1Redlight407416∗ 416Data set1Greenlight211416∗ 416Data set1Yellowlight158416∗ 416Data set2navLlight451416∗ 416Data set2navRlight449416∗ 416To test whether the network model obtained by deep learning using simulated ink symbolic images can detect the ink symbols in the actual captured images, as well as directly use the ink symbols as a whole as a detection target. The side view is divided into two kinds, in this paper, the target is located on my left or right side, and the two kinds of targets are recorded as navLlight and navRlight, and the dataset is labeled and divided into the training set and test set in the ratio of 10 : 1.


## 4.3. Experiment and Result Analysis

In this paper, we use the Darknet framework with pretrained model structure tiny-YOLOv3, and pretrained model parameter weight YOLOv3-tiny.Cont.15 to train and detect the overall ink symbols and individual ink symbols in turn for the experiments. In the preprocessing stage, the three parameters, saturation, exposure, and hue, are set for data enhancement.In order to objectively evaluate the effect of detection and recognition, three evaluation indicators are used here, namely, the accuracy rateCr, the false detection rate Fr, and the missed detection rate Lr. Suppose the total number of ink symbolic targets in the detection data is N, and the number of correctly detected ink symbols is Nc. The number of false detections is Nf, and the number of missed detections is Nl [8]. Then, the accuracy rate is calculated as(2)Cr=NcN.The false detection rate is calculated by the formula(3)Fr=NfNf+N.The formula for calculating the leakage rate is(4)Lr=NlN.The training set of dataset 2 is trained, and the training is stopped after 18 hours and 40,000 iterations under GPU acceleration to obtain the target detection model.First, the test set of dataset 2 was used for testing, and some of the results were rendered for comparison as shown in Figure4. Then, the side view image of the in-air dragging ink painting ink symbols with an ink painting length longer than or equal to 50 m and a dragging length greater than 200 m was selected for testing because this group of ink symbols and the ink symbols of the dataset 2 have high similarity, and its ink symbols contain the ink symbol features of dataset 2, and the detection results are shown in Figure 4. Finally, the actual shot with the highest quality in the side view image of the ink painting is selected for testing, and the detection results are shown in Figure 5. According to the detection results in Figure 4, the network model cannot correctly recognize the ink symbolic images that contain other ink symbols besides navRlight and navLlight and will misdetect them as navRlight and navLlight. Without adding negative samples in this training, the model will recognize the other ink symbols that appear as background and will not determine them as a whole.Figure 4
Comparison of the overall rendering results of the ink symbols of the simulated image.Figure 5
The overall inspection result of the actual side view of the normally drawn ink painting.In the actual ink painting detection, the left image correctly detects that the ink symbol is located on the left side, but the reflection of the ink symbol on the water surface is mistakenly detected as part of the ink symbol, which also appears in the detection frame. The right image has a large number of bright clouds in the sky, which interferes with the model, and the ink symbol is a very small target relative to the whole image and is not detected [32].It can be seen that the ink symbol as a whole is a detection target, a one-time realization of the detection of ink symbols and the type of ink symbols represented by them to discriminate the existence of greater difficulty. The most important reason is the variety of ink symbols. The actual situation as needed will be the presence of other lighting equipment and lights, which can cause interference with the correct detection of ink symbols. Secondly, due to some ink symbols of ink symbols in some angles, there is a high degree of similarity. For example, in the mooring in the execution of a pilotage task, ink painting side view and normal ink painting of the left side view, the number and color of ink symbols are the same, only the relative position between the lights is different. These characteristics are not learned by the model. Finally, the water reflection, sky clouds, and other background noise with relatively large brightness also affect the correct recognition of the ink symbols [33].


## 4.4. Training and Detection of Single Ink Symbols

Training is performed on dataset 1, and the model is iterated 500000 times to reach the optimum model, and the target detection model is obtained. The test set of dataset 1 is tested, and some results are shown in Figure6. The actual scene image is tested as shown in Figure 7. In the clean background scene of Figure 6, the foremast light, mizzen light, and outboard light are correctly identified, no false detection and no missed detection. But the partial reflection of the ink symbol on the water surface and a nonink symbol light fixture in the figure are mistakenly detected as ink symbols, and the shore light in the figure is mistakenly detected as ink symbols.Figure 6
Single ink symbol target detection conversion results.Figure 7
Ink painting actual image style conversion results.For the scene where ink symbols are reflected on the water surface in Figure7, the shape and size features of the water surface reflection are used to reject them, i.e., image preprocessing. The original input image is transformed into a grayscale image, and after the threshold operation using the Otsu method [13], the contour detection is performed, and the detected contour is judged by the roundness and external rectangle area. The detection effect after the completion of the preprocessing operation is shown in Figure 8.Figure 8
Image preprocessing detection effect in water reflection scene.Collected maritime ink symbolic images were preprocessed and then detected, and the statistical results are shown in Table2. In the detection of the actual captured ink symbol images, the accuracy of the individual ink symbol detection method reaches 63.79%, which is much higher than the overall ink symbol detection algorithm, and the false detection rate and leakage rate are lower than the overall ink symbol detection methods. The main reason for the high miss rate of the single ink symbol target detection algorithm is that the ink symbols in some scenes are only a few pixels in size, which is a very small target in a 416∗416 image, and the detection difficulty is high.Table 2
Comparison of experimental results.
Experimental objectNumber of iterationsTraining timeDetection typeNumber of testsAccuracy (%)False detection rate (%)Missed detection rate (%)Number of recognized images per secondIntegral light4000018 hSimulation1471.4322.220.080.0Actual333.035.7190.9135.59Single signal lamp500000222HSimulation8381.936.7410.8410.68Actual5863.793.3332.7697.19The test results show that the deep learning method based on YOLOv3 can be applied to the recognition and detection of ink symbols at sea, and the recognition of individual ink symbols can improve the detection accuracy, which can more flexibly cope with the detection of ink symbols under different ink symbol types and states.


## 5. Conclusions

Accurate and fast recognition of ink symbols can enhance the perception of intelligent ink symbols to the environment, provide information input for the operation of intelligent ink symbols, and improve the perfection of ink painting. There is still a gap in the research of using computer vision to recognize ink symbols at home and abroad. This paper proposes an end-to-end deep neural network model based on YOLOv3 for overall and individual recognition of ink symbols from a computer vision perspective. Ink symbol images generated by the simulation software are used for learning, and the overall and individual ink symbol detection models are obtained. The YOLOv3 detection algorithm has a good detection effect on the ink symbolic targets and provides a preliminary solution to the problem of ink symbol information perception.



---

*Source: 4731463-2022-02-12.xml*