Patent Publication Number: US-2023162488-A1

Title: Closed loop automatic dataset creation systems and methods

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is a continuation of U.S. patent application Ser. No. 17/146,459 filed Jan. 11, 2021 and entitled “CLOSED LOOP AUTOMATIC DATASET CREATION SYSTEMS AND METHODS,” which is a continuation of International Patent Application No. PCT/US2019/041087 filed Jul. 9, 2019 and entitled “CLOSED LOOP AUTOMATIC DATASET CREATION SYSTEMS AND METHODS,” which claims priority to U.S. Provisional Patent Application 62/697,379 filed Jul. 12, 2018 and entitled “CLOSED LOOP AUTOMATIC DATASET CREATION SYSTEMS AND METHODS,” all of which are incorporated herein by reference in their entirety. 
     This application also is a continuation-in-part of U.S. patent application Ser. No. 17/127,684 filed Dec. 18, 2020 and entitled “SYNTHETIC INFRARED DATA FOR IMAGE CLASSIFICATION SYSTEMS AND METHODS,” which is a continuation of International Patent Application No. PCT/US2019/037555 filed Jun. 17, 2019 and entitled “SYNTHETIC INFRARED DATA FOR IMAGE CLASSIFICATION SYSTEMS AND METHODS,” which claims the benefit of U.S. Provisional Application No. 62/686,627 filed Jun. 18, 2018 and entitled “SYNTHETIC INFRARED DATA FOR IMAGE CLASSIFICATION SYSTEMS AND METHODS,” all of which are hereby incorporated by reference in their entirety. 
    
    
     TECHNICAL FIELD 
     One or more embodiments of the present disclosure relate generally to image classification, and more particularly, for example, to systems and methods for training and/or validating a neural network for image classification. 
     BACKGROUND 
     In the field of image processing, there is an ongoing need for efficient and reliable ways to detect and classify objects of interest within a field of view (e.g., a scene) of an imaging device. In one approach, various images of an object of interest are collected into a training dataset for training a neural network to classify the object. The training images may be generated with a camera capturing images of the object at various angles and in various setting. A training dataset often includes thousands of images for each object classification, and can be time consuming, expensive and burdensome to produce. Some training images may be impractical to capture using a camera due to a risky location, an attempt to capture an image of a rare and unpredictable occurrence, or a large number of imaging scenarios that are needed. Further, training a neural network can be time consuming and processing intensive, taking several days or longer to work through a training dataset. As a result, such image classification neural networks may be difficult to adapt to new objects and environments and/or update with new training data. In view of the foregoing, there is a continued need for improved object detection and classification solutions that are easily adaptable to new use cases and that provide performance or other advantages over conventional systems. 
     SUMMARY 
     Various techniques are provided for training a neural network to classify images. In one or more embodiments, a convolutional neural network (CNN) is trained using training dataset comprising a plurality of synthetic images. The CNN training process tracks image-related metrics and other informative metrics as the training dataset is processed. The trained inference CNN may then be tested using a validation dataset of real images to generate performance results (e.g., whether the inference accurately classify objects of interest). In one or more embodiments, a training dataset and analysis engine extracts and analyzes the informative metrics and performance results, generates parameters for a modified training dataset to improve CNN performance, and generates corresponding instructions to a synthetic image generator to generate a new training dataset. The process repeats in an iterative fashion to build a final training dataset for use in training an inference CNN. 
     In one or more embodiments, synthetic imagery is generated to train a neural network (such as a CNN) for image classification. In one or more embodiments, an image classification system comprises a neural network trained on a synthetic training dataset, including synthetic images of objects rendered from a virtually represented imaging sensor in a virtual three-dimensional scene. In some embodiments, synthetic images include synthetic visible images and/or synthetic infrared images generated using infrared radiation signatures of virtual objects in the virtual three-dimensional scene and an infrared response model of the virtually represented infrared sensor. In one or more embodiments, a system for generating synthetic infrared training data comprises a three-dimensional scene modeling system operable to generate three-dimensional scenes comprising a plurality of objects, each object having an infrared radiation model, and an infrared sensor modeling system operable to model an imaging response for an infrared sensor virtually represented in the three-dimensional scene. 
     The scope of the invention is defined by the claims, which are incorporated into this section by reference. A more complete understanding of embodiments of the invention will be afforded to those skilled in the art, as well as a realization of additional advantages thereof, by a consideration of the following detailed description of one or more embodiments. Reference will be made to the appended sheets of drawings that will first be described briefly. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG.  1    illustrates a neural network training and validation system and process for a neural network, in accordance with various embodiments of the present disclosure. 
         FIG.  2    illustrates an exemplary system for generating a set of synthetic image data for training an image classification system, in accordance with various embodiments of the present disclosure. 
         FIGS.  3 A-B  illustrate exemplary image classification systems trained using synthetic image data, in accordance with various embodiments of the present disclosure. 
         FIG.  4 A  illustrates a neural network training process using synthetic images, in accordance with various embodiments of the present disclosure. 
         FIG.  4 B  illustrates a validation process for the neural network of  FIG.  4 A , in accordance with various embodiments of the present disclosure. 
         FIG.  4 C  illustrates a neural network training process using synthetic images, in accordance with various embodiments of the present disclosure. 
         FIG.  4 D  illustrates an operation of the trained neural network of  FIG.  4 C , in accordance with various embodiments of the present disclosure. 
         FIG.  5    illustrates an exemplary process for generating synthetic training data for object classification, in accordance with various embodiments of the present disclosure. 
         FIG.  6    illustrates an imaging system for use with an image classification system trained using synthetic image data, in accordance with various embodiments of the present disclosure. 
     
    
    
     Embodiments of the disclosure and their advantages are best understood by referring to the detailed description that follows. It should be appreciated that like reference numerals are used to identify like elements illustrated in one or more of the figures. 
     DETAILED DESCRIPTION 
     Aspects of the present disclosure generally relate to image classification, and more particularly, for example, to systems and methods for training and validating a neural network for image classification. In one or more embodiments, synthetic images of a virtual three-dimensional environment are generated and used to train a neural network (such as a convolutional neural network (CNN)) for image classification. Performing video and/or image analytics using a CNN generally includes thousands of diverse training images to achieve an acceptable level of accuracy for many use environments, but capturing such imagery using a physical imaging device to image a real world scene can be very expensive. In accordance with various embodiments disclosed herein, a synthetic training dataset is generated to train a CNN, the training results are validated, and a training dataset analysis engine analyzes informative metrics and performance results to determine parameters for a new training dataset. The new parameters may be used to generate instructions for a synthetic image generator to update the training dataset to train an inference CNN with improved performance results. 
     Referring to  FIG.  1   , various embodiments of a system for training and validating a neural network will be described. In one or more embodiments, a system  50  generates a training dataset in an iterative process that yields high performance CNN object classification. The system  50  includes a synthetic image generator  52  (e.g., the environment simulation system  102  in  FIG.  2   ) used to generate a training dataset  56  for training a neural network in a training process  58 . The training process  58  works through the training dataset  56  to produce a trained inference CNN  60 , and also generates image-specific data and other informative metrics during the training process. The trained inference CNN  60  is validated using a validation dataset  62  of labeled images to measure the performance of the inference CNN  60  as an image classifier. 
     In various embodiments, the validation dataset  62  includes a plurality of labeled, real world, images that are input to the trained inference CNN  60  and classified to measure of the performance of the trained inference CNN  60 . The validation test images may include a variety of objects, object sizes and backgrounds representing real world use cases for the inference CNN  60 . The real world images may be captured using any image capture device as appropriate including devices generating visible and/or infrared images. The performance results, including proper image labeling and image classification errors are provided to the training dataset analysis engine  70 . The training dataset analysis engine  70  also receives image-specific data and other informative metrics compiled during the training process  58 , and configuration parameters  64  that define a scope of use for the trained inference CNN  60 . In one embodiment, a performance evaluator  66  receives the output of the inference CNN  60  and ground truth annotations from the validation dataset  62  to produce performance results data that is provided to the training dataset analysis engine  70 . 
     The training dataset analysis engine  70  may then analyze the received data to modify the training dataset  56  by identifying images to keep (e.g., images that contribute to proper classification), drop from (e.g., images that do not contributed to proper classification) and/or add to the training dataset  56 . In one or more embodiments, the training dataset analysis engine  70  receives the informative metrics and performance results, analyzes the available data in view of the configuration parameters, and instructs the synthetic image generator  52  to produce an updated training dataset  56  that is predicted to train an inference CNN with improved results. 
     In various embodiments, the training dataset analysis engine  70  includes a data extractor/analyzer  72 , a dataset generator  74 , and an assembler/interface  76 . In one or more embodiments, the data extractor/analyzer  72  receives the informative metrics and performance results, extracts features for further processing, and analyzes the relative performance of one or more images from the training dataset  56 . Metrics may include, for example, extracted features, data indicating changes in neural network parameters, data from previous iterations, and other data captured during training. In some embodiments, the extractor/analyzer  72  ranks the images from the training dataset  56  based on the performance results and/or the effect the image had on the training of the neural network. The dataset generator  74  uses the results of the data extractor/analyzer  72  and, in view of configuration parameters  64 , generates parameters for a new training dataset  56  comprising a subset of the current training dataset  56  images and parameters defining new synthetic images to be generated for the next training dataset. The assembler/interface  76  converts the new training dataset parameters into instructions directing the image creation interface  54  to cause the synthetic image generator  52  to generate a new training data set  56 . In some embodiments, the process continues iteratively until a final training dataset  80  that meets certain performance criteria, such as a percentage of correctly classified images during the validation process, performance for various size objects, cost validation and/or other criteria, is generated. 
     In various embodiments, the data extractor/analyzer  72  may extract informative metrics and/or performance results into various categories for further analysis, including compiling data based on different classification labels of the images from the training dataset, data based on performance/underperformance, image characteristics (e.g., image size, object size, features extracted), and other groupings as may be appropriate. In one or more embodiments, the dataset generator  74  is one or more algorithms, neural networks, and/or other processes that receive the informative metrics and performance results and determines how to modify the training dataset to improve performance. The configuration parameters  64  define one or more goals of the classification CNN, such as parameters defining labels, objects, and environments to be used in the training dataset. For example, the configuration parameters  64  can be used to determine what assets the neural network should classify (e.g., people, bicycles, vehicles, roads, animals) and background environment in which the assets should appear (e.g., buildings, sky, trees). 
     In various embodiments, the synthetic image generator  52  may receive instructions to create a new training dataset  56 , including an identification of current dataset images to maintain and which current images to delete. The synthetic image generator  52  may also receive instructions to generate new synthetic images in accordance with received parameters. In various embodiments, synthetic image generation may include random image generation, informed by configuration parameters  64  and an identification of desirable and undesirable parameters as defined by the dataset generator  74 . For example, the inference CNN  60  may be trained to label certain objects (e.g., people, animals, vehicles), which may be presented in a variety of real world backgrounds, and the current training dataset may have produced unacceptable results classifying people in certain backgrounds. The synthetic image generator  52  may be instructed to create images of a certain object classification (e.g., people) by generating random versions of the object (e.g., men/women, young/old, different clothing, etc.) from random angles and distances, with a random background environments in accordance with the received parameters. In various embodiments, the synthetic image generator  52  is not constrained to producing images representing real world scenarios. Because a goal of the system is to train an inference CNN for classification, it is recognized that a synthetic image representing an unrealistic scenario (e.g., a flying pig) may be useful to training the CNN if it improves the results. 
     In some embodiments, the dataset generator  74  determines a subset of images from the training dataset  56  to maintain in the training dataset and defines new images to be generated. In some embodiments, images from the training dataset  56  may be ranked on performance results by ranking each image&#39;s impact based on overall performance. For example, the dataset generator  74  may keep a certain number of top ranked images for each image classification, keep images that contribute above an identified performance threshold, and/or keep a certain number of top ranked images overall. The dataset generation module  74  may also remove images from the training dataset  56  that are lowest ranked and/or contribute negatively or below an identified performance threshold. For example, the training dataset analysis engine  70  may ranked 1000 images, for example, and keep the best 100 images, remove the remaining 900 images. The new training dataset can include the previous 100 images and the newly generated  900  synthetic images which are selected to improve performance. Various parameters may represent a general categories for random scene generation including foreground object, size, type and angle; the position and type of the camera (e.g., visible camera, infrared imaging device); and presence of background assets. 
     It will be appreciated by those skilled in the art that creation of a synthetic training dataset and training a neural network is both time consuming and a processing intensive process, which can take days or weeks to perform. The processes described herein greatly reduce the time required to generate a training dataset and improve the performance of the resulting trained inference CNN. 
     Referring to  FIG.  2   , embodiments of a synthetic imaging system  100  for generating a synthetic training dataset will be described. As illustrated, the synthetic imaging system  100  includes an environment simulation system  102  operable through a user interface port  110 . In various embodiments, the environment simulation system  102  may include one or more computing devices operable to generate a three-dimensional (3D) environment simulating real world settings. The 3D environment may be constructed from a plurality of objects, including terrain, buildings, vehicles, people, animals and other objects that may appear in a captured image of a real world scene. In various embodiments, the synthetic imaging system  100  is configured to generate synthetic images that simulate images captured from a real image capture devices. 
     In various embodiments, the environment simulation system  102  includes a scene generator  104 . The scene generator  104  is operable to build a virtual 3D environment utilizing data from an object database  112 , which stores 3D models and other object data allowing a modeled 3D object to be placed in the scene. The scene generator  104  may also apply environmental effects  114  (such as weather conditions, temperature, time of day, etc.). The environment simulation system  102  may optionally include an infrared sensor simulator/image capture component  106  for capturing infrared images of a scene and/or an optical image capture component  108  for capturing visible images of a generated scene. 
     The infrared sensor simulator  106  is used to render a synthetic infrared image of the generated scene from the point of view of an infrared sensor. An infrared sensor model for each real world camera or imaging device is created and stored in the sensor models database  116  and may include sensor properties such as the wavelengths detected and pixel resolution of the physical device. In operation, a scene is created, environmental conditions are applied and a virtual infrared camera is positioned within the scene. The infrared radiation (e.g., heat) generated from the surface of each object in the scene is propagated to simulate real life heat transfer (e.g., by applying heat ray tracing from each surface), thermal signatures and other infrared radiation. In some embodiments, a motion factor may be applied to model infrared radiation as one or more objects move through the scene. A synthetic infrared image captures the infrared radiation in the scene from the position of the virtual infrared camera as modeled by an appropriate sensor model. 
     In various embodiments, infrared sensor models are created to approximate real world sensor response for a variety of infrared sensors, such as long wave, medium wave and short wave infrared sensors. The sensor models may also model active and passive infrared sensor systems, different wavelengths, such as near-infrared (e.g. night vision), and different physical detectors (e.g., microbolometers, quantum well, diode detectors). The infrared sensor simulator  106  can also combine infrared with any other modality (to cover both infrared and visible, for example). In some embodiments, noise is added to a subset of the synthetic infrared images to account for manufacturing tolerances and other variations affecting the operation of real world infrared sensors. For example, an embodiment may randomly apply one of ten types of noise to a subset of images to simulate a range of images captured from real world infrared cameras. Noise may also be added to account for other system and imaging variations, including non-realistic effects, to cause the system to learn to recognize objects regardless of its surroundings and obscurants. 
     In some embodiments, the optical image capture component  108  may be used to generate a synthetic visible light image of the scene. In embodiments that include the capture of both visible and infrared images, a synthetic visible light image may be stored with a synthetic infrared image for use in object classification. In other embodiments, the infrared sensor simulator  106  and optical image capture component  108  may operate to model a multi spectral dynamic imaging system (MSX) which generates a single combined image including a radiometric component of the captured synthetic infrared image and a blended component including infrared (e.g., thermal) characteristics of the scene blended with the visible light image. 
     In various embodiments, an object stored in the object database  112  includes a wire mesh model comprising a plurality of polygons, such as triangles. The wire mesh model includes a skin that reflects visible light, allowing the object to be visually represented in a three-dimensional scene. For infrared imaging, the infrared radiation models are applied to the three-dimensional objects in the scene. The infrared radiation models database  118  stores models for simulating the radiation transmitted from the three-dimensional objects, including fields associating objects with the infrared radiation models. In one or more embodiments, each triangular surface of an object is modeled using properties of the object&#39;s surface emissivity and/or expected temperature. For example, for each triangle, a model is created based on the emissivity of the particular skin, and the infrared radiation may be propagated in the scene through a ray tracing process from each triangular surface. 
     The properties of the object&#39;s skin on the three-dimensional wire mesh structure may be associated with real world infrared radiation models. A challenge is to create models for every object that may appear in a scene. One approach is to capture real world infrared images of each object to be modeled, however, this may be time consuming and expensive. In one embodiment, a three-dimensional object includes a surface skin having known texture and other properties, and the system applies an infrared radiation model generally associated with the surface skin and/or a general object classification. For example, an infrared radiation model for a mammal with fur may be applied to other mammals with similar fur characteristics. In one embodiment, a generalized model may be used for new objects. If feedback from a validation process indicates that the generalized model is not producing accurate image classification results, then a new model may be created or applied for the object to reduce the system error. The models may be updated as necessary to train a system for accurate image classification. 
     As described further herein, the embodiments of the present disclosure are scalable, allowing for creation of synthetic images that are accurate enough for image classification while optimizing performance within the constraints of a practical system. In one embodiment, object resolution (e.g., the size of the triangles) is scalable to optimize the operation of the environment simulation system  102  in view of time, processing, bandwidth and storage constraints. Many objects may be accurately modeled with a lower resolution (e.g., larger triangles and/or applying fewer rays traced per triangle), and complexity may be added as desired for accurate image classification. A building object, for example, may not require a model of the emissivity of every brick, provided enough features are modeled to allow the image classification system to properly detect or ignore the object. As another example, in many applications such as a system for identifying people, objects such as trees don&#39;t require a high degree of accuracy in modeling (e.g., don&#39;t need to model every leaf) and may be modeled at a lower complexity than other objects. By applying a range of complexity to the thermal modeling, an accurate and practical system may be created. 
     In operation, an operator accesses a user interface port  110  and establishes training parameters through a dataset creation control interface  120 . Training parameters may include a location, environment, objects to be detected and other factors likely to appear in an image captured by an image capture device, such as a surveillance system. The dataset creation control interface  120  generates a series of images with a variety of locations, objects, angles, distances, and the images are stored in a synthetic image database  122  along with annotations for each image, including the object to be detected from the image and various scene, environmental data and dataset identifiers. The captured synthetic images may be compiled to produce a training dataset for use in training a neural network or other image classification system. In some embodiments, the training dataset may also include real images  124  captured with a camera, optical images captured by the optical image capture component  108 , and other data as appropriate. 
     In various embodiments, it is desirable to have a training set comprising synthetic images of many different scenes. The environment simulation system  102  may be configured to create scenes using randomly created environments including randomness in the number and types of objects in the scene, location of objects, position of camera, and other factors affecting the scene to create a unique image. In some embodiments, non-realistic scenes, backgrounds and effects are imaged to expand the training dataset and create entropy. 
     The synthetic imaging system  100  may be embodied on one or more computing devices, servers and/or one or more databases, and may be combined with other components in an image classification system. Referring to  FIG.  3 A , various embodiments of an image classification system  200  will be described. The image classification system  200  may be implemented on one or more servers such as an application server that performs data processing and/or other software execution operations for generating, storing, classifying and retrieving images. In some embodiments, the components of the image classification system  200  may be distributed across a communications network, such as network  222 . The communications network  222  may include one or more local networks such as a wireless local area network (WLAN), wide area networks such as the Internet, and other wired or wireless communications paths suitable for facilitating communications between components as described herein. The image classification system  200  includes communications components  214  operable to facilitate communications with one or more network devices  220  over the communications network  222 . 
     In various embodiments, the image classification system  200  may operate as a general purpose image classification system, such as a cloud-based image classification system, or may be configured to operate in a dedicated system, such as a video surveillance system that stores video and images captured in real time from a plurality of image capture devices and identifies and classifies objects using a database  202 . The image classification system  200  may be configured to receive one or more images (e.g., an image captured from infrared camera of a video surveillance system or a visible light image) from one or more network devices  220  and process associated object identification/classification requests. 
     As illustrated, the image classification system  200  includes one or more processors  204  that perform data processing and/or other software execution operations for the image classification system  200 . The processor  204  may include logic devices, microcontrollers, processors, application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs) or other devices that may be used by the image classification system  200  to execute appropriate instructions, such as software instructions stored in memory  206  including 3D simulation and image capture component  208 , training dataset generation component  210 , and image classification component  212  (e.g., a neural network trained by the training dataset), and/or other applications. The memory  206  may be implemented in one or more memory devices (e.g., memory components) that store executable instructions, data and information, including image data, video data, audio data, network information. The memory devices may include various types of memory for information storage including volatile and non-volatile memory devices, such as RAM (Random Access Memory), ROM (Read-Only Memory), EEPROM (Electrically-Erasable Read-Only Memory), flash memory, a disk drive, and other types of memory described herein. 
     Each network device  220  may be implemented as a computing device such as a desktop computer or network server, a mobile computing device such as a mobile phone, tablet, laptop computer or other computing device having communications circuitry (e.g., wireless communications circuitry or wired communications circuitry) for connecting with other devices in image classification system  200 . In various embodiments, the network device  220  may include an imaging device or a component of a video surveillance system. 
     The communications components  214  may include circuitry for communicating with other devices using various communications protocols. In various embodiments, communications components  214  may be configured to communicate over a wired communication link (e.g., through a network router, switch, hub, or other network devices) for wired communication purposes. For example, a wired link may be implemented with a power-line cable, a coaxial cable, a fiber-optic cable, or other appropriate cables or wires that support corresponding wired network technologies. Communications components  214  may be further configured to interface with a wired network and/or device via a wired communication component such as an Ethernet interface, a power-line modem, a Digital Subscriber Line (DSL) modem, a Public Switched Telephone Network (PSTN) modem, a cable modem, and/or other appropriate components for wired communication. Proprietary wired communication protocols and interfaces may also be supported by communications components  214 . 
     In various embodiments, a trained image classification system may be implemented in a real-time environment, as illustrated in  FIG.  3 B . The image classification system  250  may include a thermal imaging camera or other device or system operable to receive and/or generate thermal images. In the illustrated embodiment, the image classification system  250  includes a processor and memory  260 , operable to store a trained neural network and implement a neural network run-time interface  270  thereon. 
     In various embodiments, the synthetic training dataset disclosed herein may be used to train a neural network or other classification system for object classification. Referring to  FIG.  4 A , an embodiment of a neural network utilizing synthetic training data will now be described. In one embodiment, the neural network  300  is a convolutional neural network (CNN) that receives the training dataset  302  and outputs a classification for each image. The training dataset includes synthetic images as described herein, and may also include real images captured from an infrared, visible light, or other type of camera. For object classification, the images may comprise a region of interest from a captured image that includes an object to be identified. In one embodiment, the training starts with a forward pass through the neural network  300  including feature extraction  304  in a plurality of convolution layers  306  and pooling layers  308 , followed by image classification  310  in a plurality of fully connected layers  312  and an output layer  314 . Next, a backward pass through the neural network  300  may be used to update the CNN parameters in view of errors produced in the forward pass (e.g., misclassified objects). In various embodiments, other neural network processes may be used in accordance with the present disclosure. 
     An embodiment for validating the trained neural network is illustrated in  FIG.  4 B . A set of fully annotated validation test images  320  representing real world images is fed into the trained neural network  322 . The validation test images  320  include a variety of objects, object sizes and backgrounds to classify. A performance evaluation system  323  detects and analyzes errors (e.g., image classification vs. ground truth annotations) and feed the results back to the 3D environment simulation system  324  to update the synthetic model, which in turn updates the training dataset  326 . In various embodiments, detected errors may be corrected by adding more examples an object (e.g., more types of clouds), increasing the resolution of the 3D model and/or increasing the accuracy of the thermal modeling, to help distinguish between objects in an image. By adjusting the synthetic training dataset to improve accuracy, the operator can avoid costly and time consuming efforts to collect needed real world images to update the training dataset. 
     Referring to  FIG.  4 C , further details of an embodiment for training a neural network utilizing synthetic training data will now be described. A neural network  320 , such as a convolutional neural network, is trained using a training dataset  332  that includes synthetic images as described herein. The training includes with a forward pass through the neural network  330  to produce an image classification. In the illustrated embodiment, a thermal image such as a synthetic thermal image of an elephant is fed to the neural network  330  to produce a classification at the output layer. Each synthetic image is labeled with the correct classification and the output of the neural network  330  is compared to the correct label. If the neural network  330  mislabels the input image (e.g., determines that the image is a “rhinoceros” instead of an “elephant”), then a backward pass through the neural network  330  may be used to adjust the neural network to correct for the misclassification. Referring to  FIG.  4 D , a trained neural network  340 , may then be implemented in an application (i.e., a neural network inference application) on a run time environment to classify thermal images  342 . 
     Referring to  FIG.  5   , embodiments of a process for generating training data for object detection in an image in will now be described. In step  402 , an operator defines the parameters for the training dataset including an identification of the objects to be detected and classified, the imaging sensors to be modeled and use cases/environments in which images will be captured. In step  404 , a three-dimensional virtual world is constructed to model the use case/environments. Next, imagining scenarios are constructed to fill out the training dataset, including imaging sensor locations and object orientation/location within the 3D virtual world, in step  406 . For each imaging scenario, sensor models are applied and a simulated sensor response is generated in the form of a synthetic image, in step  408 . In step  410 , selected images are augmented to model various real world and/or non-realistic scenarios to train the neural network to classify objects in a variety of surroundings and obscurant conditions. For example, selected images may be augmented to add various types of noise, scale the images, flip the images, morph the images via style transfer techniques and other image augmentations. 
     In step  412 , each captured synthetic image is stored in a training set database along with annotations describing the imaging scenario. In various embodiments, the annotations may identify objects in the image, object details, modeled sensor type, 3D environment, camera location and position, background objects/environment, time of day, weather and other information as appropriate to define the imaging scenario. In step  414 , the synthetic training dataset is used to train a neural network. In step  416 , the neural network is validated using real world images and the results are used to update the parameters of the virtual world and imaging scenarios to improve performance. 
     Referring to  FIG.  6   , various embodiments of an imaging system will be described. The imaging system may be used to capture and process images to detect, classify and/or count objects that appear in a field of view. As illustrated, the imaging system  500  may be used for imaging a scene  570  in a field of view. The imaging system  500  includes a processing component  510 , a memory component  520 , an image capture component  530 , optical components  532  (e.g., one or more lenses configured to receive electromagnetic radiation through an aperture  534  in camera component  501  and pass the electromagnetic radiation to image capture component  530 ), an image capture interface component  536 , a display component  540 , a control component  550 , a communication component  552 , and other sensing components. 
     In various embodiments, imaging system  500  may be implemented as an imaging device, such as camera component  501 , to capture image frames, for example, of the scene  570  in the field of view of camera component  501 . In some embodiments, camera component  501  may include image capture component  530 , optical components  532 , and image capture interface component  536  housed in a protective enclosure. Imaging system  500  may represent any type of camera system that is adapted to image the scene  570  and provide associated image data. Imaging system  500  may be implemented with camera component  501  at various types of fixed locations and environments (e.g., highway overpass to track traffic, as part of a premises surveillance system, to monitor/track people, etc.). In some embodiments, camera component  501  may be mounted in a stationary arrangement to capture successive images of a scene  570 . Imaging system  500  may include a portable device and may be implemented, for example, as a handheld device and/or coupled, in other examples, to various types of vehicles (e.g., a land-based vehicle, a watercraft, an aircraft, a spacecraft, or other vehicle). 
     Processing component  510  may include, for example, a microprocessor, a single-core processor, a multi-core processor, a microcontroller, a logic device (e.g., a programmable logic device configured to perform processing operations), a digital signal processing (DSP) device, one or more memories for storing executable instructions (e.g., software, firmware, or other instructions), and/or any other appropriate combination of processing device and/or memory to execute instructions to perform any of the various operations described herein. Processing component  510  is adapted to interface and communicate with components  520 ,  530 ,  540 , and  550  to perform method and processing steps as described herein. Processing component  510  is also adapted to detect and classify objects in the images captured by the image capture component  530 , through image processing module  580 , object detection module  582 , and synthetically trained image classification module  584 . 
     It should be appreciated that processing operations and/or instructions may be integrated in software and/or hardware as part of processing component  510 , or code (e.g., software or configuration data) which may be stored in memory component  520 . Embodiments of processing operations and/or instructions disclosed herein may be stored by a machine readable medium in a non-transitory manner (e.g., a memory, a hard drive, a compact disk, a digital video disk, or a flash memory) to be executed by a computer (e.g., logic or processor-based system) to perform various methods disclosed herein. 
     Memory component  520  includes, in one embodiment, one or more memory devices (e.g., one or more memories) to store data and information. The one or more memory devices may include various types of memory including volatile and non-volatile memory devices, such as RAM (Random Access Memory), ROM (Read-Only Memory), EEPROM (Electrically-Erasable Read-Only Memory), flash memory, or other types of memory. In one embodiment, processing component  510  is adapted to execute software stored in memory component  520  and/or a machine-readable medium to perform various methods, processes, and operations in a manner as described herein. 
     Image capture component  530  includes, in one embodiment, one or more sensors for capturing image signals representative of an image, of scene  570 . In one embodiment, the sensors of image capture component  530  provide for representing (e.g., converting) a captured infrared image signal of scene  570  as digital data (e.g., via an analog-to-digital converter included as part of the sensor or separate from the sensor as part of infrared imaging system  500 ). Infrared sensors may include a plurality of infrared sensors (e.g., infrared detectors) implemented in an array or other fashion on a substrate. For example, in one embodiment, infrared sensors may be implemented as a focal plane array (FPA). Infrared sensors may be configured to detect infrared radiation (e.g., infrared energy) from a target scene including, for example, mid wave infrared wave bands (MWIR), long wave infrared wave bands (LWIR), and/or other thermal imaging bands as may be desired in particular implementations. Infrared sensors may be implemented, for example, as microbolometers or other types of thermal imaging infrared sensors arranged in any desired array pattern to provide a plurality of pixels. 
     Processing component  510  may be adapted to receive image signals from image capture component  530 , process image signals (e.g., to provide processed image data), store image signals or image data in memory component  520 , and/or retrieve stored image signals from memory component  520 . In various aspects, processing component  510  may be remotely positioned, and processing component  510  may be adapted to remotely receive image signals from image capture component  530  via wired or wireless communication with image capture interface component  536 , as described herein. 
     Display component  540  may include an image display device (e.g., a liquid crystal display (LCD)) or various other types of generally known video displays or monitors. Control component  550  may include, in various embodiments, a user input and/or interface device, such as a keyboard, a control panel unit, a graphical user interface, or other user input/output. Control component  550  may be adapted to be integrated as part of display component  540  to operate as both a user input device and a display device, such as, for example, a touch screen device adapted to receive input signals from a user touching different parts of the display screen. 
     Processing component  510  may be adapted to communicate with image capture interface component  536  (e.g., by receiving data and information from image capture component  530 ). Image capture interface component  536  may be configured to receive image signals (e.g., image frames) from image capture component  530  and communicate image signals to processing component  510  directly or through one or more wired or wireless communication components (e.g., represented by connection  537 ) in the manner of communication component  552  further described herein. Camera component  501  and processing component  510  may be positioned proximate to or remote from each other in various embodiments. 
     In one embodiment, communication component  552  may be implemented as a network interface component adapted for communication with a network including other devices in the network, and may include one or more wired or wireless communication components. In various embodiments, a network  554  may be implemented as a single network or a combination of multiple networks, and may include a wired or wireless network, including a wireless local area network, a wide area network, the Internet, a cloud network service, and/or other appropriate types of communication networks. 
     In various embodiments, imaging system  500  provides a capability, in real time, to detect, classify and count objects in the scene  570 . For example, imaging system  500  may be configured to capture images of scene  570  using camera component  501  (e.g., an infrared camera). Captured images may be received by processing component  510  and stored in memory component  520 . The image processing module  580  and object detection module  582  may extract from each of the captured images, a subset of pixel values of scene  570  corresponding to a detected object. The synthetically trained image classification module  584  classifies the detected object and stores the result in the memory component  520 , an object database or other memory storage in accordance with system preferences. In some embodiments, imaging system  500  may send images or detected objects over network  554  (e.g., the Internet or the cloud) to a server system, such as image classification system  556 , for remote image classification. 
     The object detection module  582  and synthetically trained image classification module  584  provide analysis of the captured images to detect and classify an object in a captured image. In various embodiments, the object detection module  582  interfaces with an object classification database, which stores information for analyzing and identifying digitized objects and other sensor information captured by an image capture device. For example, the database may store object classification criteria for generally identifying the class of a detected object (e.g., a person, an animal, a car, motorcycle, plant life, blowing objects, a shadow, etc.), reference images of known objects which may include synthetic images, field of view parameters for each image capture device (e.g., to for use in estimating object size), learned and configured activities common to each image capture device (e.g., false detections and classifications of objects may have been triggered by a nonstandard event), and other object classification information. 
     Where applicable, various embodiments provided by the present disclosure can be implemented using hardware, software, or combinations of hardware and software. Also where applicable, the various hardware components and/or software components set forth herein can be combined into composite components comprising software, hardware, and/or both without departing from the spirit of the present disclosure. Where applicable, the various hardware components and/or software components set forth herein can be separated into sub-components comprising software, hardware, or both without departing from the spirit of the present disclosure. 
     Software in accordance with the present disclosure, such as non-transitory instructions, program code, and/or data, can be stored on one or more non-transitory machine readable mediums. It is also contemplated that software identified herein can be implemented using one or more general purpose or specific purpose computers and/or computer systems, networked and/or otherwise. Where applicable, the ordering of various steps described herein can be changed, combined into composite steps, and/or separated into sub-steps to provide features described herein. 
     Embodiments described above illustrate but do not limit the invention. It should also be understood that numerous modifications and variations are possible in accordance with the principles of the invention. Accordingly, the scope of the invention is defined only by the following claims.