CN113065466B - Deep learning-based traffic light detection system for driving training - Google Patents
Deep learning-based traffic light detection system for driving training Download PDFInfo
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Abstract
The invention relates to traffic light detection, in particular to a traffic light detection system for driving training based on deep learning, which comprises a controller, wherein the controller receives training images and detection images respectively sent by a training image acquisition module and a detection image acquisition module through an image preprocessing module, the controller is connected with a first image characteristic acquisition module for acquiring characteristics of the training images, the controller is connected with a recognition model construction module for constructing a signal recognition model, the controller is connected with a recognition result checking module for receiving and checking recognition results of the signal recognition model, and the controller is connected with a recognition model optimization module for optimizing the signal recognition model according to the checking results; the technical scheme provided by the invention can effectively overcome the defect that the state of the traffic signal lamp cannot be accurately and effectively identified in the prior art.
Description
Technical Field
The invention relates to traffic light detection, in particular to a driving training traffic light detection system based on deep learning.
Background
The identification of the traffic signal lamp refers to identifying the state of the traffic signal lamp on the basis of accurately positioning the traffic signal lamp, for example, for the most common traffic signal lamp in the form of a traffic light, the identification of the traffic signal lamp specifically refers to determining the indication state (such as allowing traffic, prohibiting traffic and the like) of the traffic signal lamp by identifying the bright and dark state (such as red light, green light, bright and dark of yellow light and the like) of the traffic signal lamp. The identification of the traffic signal lamp can be used for judging the traffic state at the traffic intersection, and has important significance in the aspects of automatic driving, navigation prompt, driving training and the like.
At present, signal lamp identification in driving training mainly depends on deep learning, signal lamp images at traffic intersections are acquired through cameras installed at fixed points, color images are input into a neural network model for deep learning to acquire states of traffic signal lamps, and broadcasting is carried out on all vehicles. However, because the outlines of the traffic lights in the color images shot by the cameras are often blurred, the accuracy of positioning the traffic lights is reduced, and the states of the traffic lights cannot be accurately identified.
Disclosure of Invention
Aiming at the defects existing in the prior art, the invention provides a driving traffic light detection system based on deep learning, which can effectively overcome the defect that the state of a traffic signal lamp cannot be accurately and effectively identified in the prior art.
In order to achieve the above purpose, the invention is realized by the following technical scheme:
The driving training traffic light detection system based on deep learning comprises a controller, wherein the controller is connected with a first image characteristic acquisition module for acquiring characteristics of training images, and is connected with a recognition model construction module for constructing a signal recognition model, the controller is connected with a recognition result checking module for receiving and checking recognition results of the signal recognition model, and the controller is connected with a recognition model optimizing module for optimizing the signal recognition model according to the checking results;
The controller is connected with a second image feature acquisition module for carrying out feature acquisition on the detected image, the controller is connected with a recognition area judgment module for judging a recognition area in the detected image according to the image features, and the controller is connected with a recognition accuracy judgment module for judging the recognition accuracy of the optimized signal recognition model;
The controller is connected with a standard template acquisition module for acquiring standard images of the signal lamps, the controller is connected with a recognition area acquisition module for acquiring recognition areas in detection images, and the controller is further connected with a comparison analysis module for comparing and analyzing the standard images of the signal lamps and the recognition areas in the detection images, the controller is connected with a color recognition module for recognizing colors of the recognition areas in the detection images, and the controller is connected with a comprehensive judgment module for comprehensively judging the states of the signal lamps according to comprehensive color recognition results of comparison analysis results.
Preferably, the first image feature acquisition module receives the training image which is sent by the image preprocessing module after preprocessing, and sends the image features extracted from the training image to the signal recognition model constructed by the recognition model construction module, and the recognition result checking module checks the recognition result of the signal recognition model.
Preferably, the recognition result checking module receives the input result of the signal lamp state in the training image manually and checks the input result with the recognition result of the signal recognition model;
When the recognition result checking module judges that the recognition result of the signal recognition model is inaccurate, the recognition model optimizing module optimizes the signal recognition model through a random gradient descent method.
Preferably, after the identification area determination module determines the identification area in the detection image, the identification area image in the detection image is sent to an optimized signal identification model, and the signal identification model identifies the signal lamp state in the identification area image.
Preferably, the recognition accuracy judging module judges that the optimized signal recognition model has errors on the recognition result of the signal lamp state in the recognition area image, and the controller starts the standard template collecting module, the recognition area collecting module and the color recognition module.
Preferably, the standard template collecting module collects standard images of all types of signal lamps from a standard image library, the identification area collecting module receives the identification areas in the detection images sent by the identification area judging module, the comparison analysis module analyzes and matches the identification areas in the detection images in the standard images of all types of signal lamps, and the matched standard images of the signal lamps are sent to the comprehensive judging module.
Preferably, the system further comprises a recognition result output module connected with the controller and used for outputting a recognition result of the signal lamp state, when the recognition accuracy judging module judges that the recognition result of the signal recognition model is accurate, the recognition result output module directly outputs the recognition result of the signal recognition model, otherwise, the recognition result output module outputs a comprehensive judgment result of the comprehensive judging module on the signal lamp state.
Preferably, the training image acquisition module acquires training images from the training image library randomly and sends the training images to the image preprocessing module, and the detection image acquisition module sends the acquired detection images containing the signal lamps to the image preprocessing module.
Preferably, the image preprocessing module performs image noise reduction and image enhancement processing on the training image and the detection image.
Compared with the prior art, the driving training traffic light detection system based on deep learning provided by the invention can effectively train the signal identification model, fully optimize the signal identification model according to the accuracy of the signal lamp identification result, effectively judge the identification area in the detection image and provide guarantee for accurately identifying the signal lamp state; when the signal lamp state identification result is wrong, the signal lamp state can be identified more accurately by matching with various signal lamp standard images and identifying colors, so that the defect that the deep learning network model is low in accuracy in the initial use period is effectively overcome.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. It is evident that the drawings in the following description are only some embodiments of the present invention and that other drawings may be obtained from these drawings without inventive effort for a person of ordinary skill in the art.
FIG. 1 is a schematic diagram of a system according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. It will be apparent that the described embodiments are some, but not all, embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
A driving training traffic light detection system based on deep learning is shown in fig. 1 and comprises a controller, wherein the controller receives training images and detection images sent by a training image acquisition module and a detection image acquisition module through an image preprocessing module, the controller is connected with a first image feature acquisition module for carrying out feature acquisition on the training images, the controller is connected with a recognition model construction module for constructing a signal recognition model, the controller is connected with a recognition result checking module for receiving and checking recognition results of the signal recognition model, and the controller is connected with a recognition model optimization module for optimizing the signal recognition model according to the checking results.
The first image feature acquisition module receives the training image which is sent by the image preprocessing module and is preprocessed, and sends the image features extracted from the training image to the signal recognition model constructed by the recognition model construction module, and the recognition result checking module checks the recognition result of the signal recognition model.
The recognition result checking module receives the input result of the signal lamp state in the training image by a person and checks the input result with the recognition result of the signal recognition model;
when the recognition result checking module judges that the recognition result of the signal recognition model is inaccurate, the recognition model optimizing module optimizes the signal recognition model through a random gradient descent method.
According to the technical scheme, the training image library is stored with training images only comprising the outer contours of the signal lamps of the signal lamp body, and the judgment result of the human operator on the signal lamp state in the training images is printed at the appointed position in the training images.
The controller is connected with a second image feature acquisition module for carrying out feature acquisition on the detected image, the controller is connected with a recognition area judgment module for judging a recognition area in the detected image according to the image features, and the controller is connected with a recognition accuracy judgment module for judging the recognition accuracy of the optimized signal recognition model.
After the identification area judgment module judges the identification area in the detection image, the identification area image in the detection image is sent to the optimized signal identification model, and the signal identification model identifies the signal lamp state in the identification area image.
According to the technical scheme, the training image acquisition module acquires training images randomly from the training image library, sends the training images to the image preprocessing module, and the detection image acquisition module sends the acquired detection images containing signal lamps to the image preprocessing module, and the image preprocessing module carries out image noise reduction and image enhancement processing on the training images and the detection images.
The controller is connected with a standard template acquisition module for acquiring standard images of the signal lamps, is connected with a recognition area acquisition module for acquiring recognition areas in detection images, and further comprises a comparison analysis module for comparing and analyzing the standard images of the signal lamps and the recognition areas in the detection images, and is connected with a color recognition module for recognizing colors of the recognition areas in the detection images, and is connected with a comprehensive judgment module for comprehensively judging the states of the signal lamps according to comprehensive color recognition results of the comparison analysis results.
When the recognition accuracy judging module judges that the optimized signal recognition model has errors on the recognition result of the signal lamp state in the recognition area image, the controller starts the standard template collecting module, the recognition area collecting module and the color recognition module.
The standard template acquisition module acquires standard images of all types of signal lamps from the standard image library, the identification area acquisition module receives the identification areas in the detection images sent by the identification area judgment module, the comparison analysis module analyzes and matches the identification areas in the detection images in the standard images of all types of signal lamps, and the matched standard images of the signal lamps are sent to the comprehensive judgment module.
When the signal lamp state identification result is wrong, the signal lamp state can be identified more accurately by matching with various signal lamp standard images and identifying colors, so that the defect that the deep learning network model is low in accuracy in the initial use period is effectively overcome.
And the comprehensive judgment module is used for comprehensively judging the signal lamp state according to the comparison analysis result comprehensive color recognition result, and inputting the result obtained by comprehensive judgment of the signal lamp state and the corresponding detection image into the optimized signal recognition model for model training so as to continuously improve the accuracy of the deep learning network model in the early stage of use for signal lamp state recognition.
In the technical scheme of the application, the signal lamp state recognition system further comprises a recognition result output module which is connected with the controller and used for outputting the recognition result of the signal lamp state, when the recognition accuracy judging module judges that the recognition result of the signal recognition model is accurate, the recognition result output module directly outputs the recognition result of the signal recognition model, otherwise, the recognition result output module outputs the comprehensive judgment result of the comprehensive judging module on the signal lamp state.
The above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (6)
1. Driving is with traffic lights detecting system based on degree of depth study, its characterized in that: the system comprises a controller, wherein the controller is connected with a first image characteristic acquisition module for acquiring characteristics of a training image, a recognition model construction module for constructing a signal recognition model, a recognition result checking module for receiving and checking the recognition result of the signal recognition model, and a recognition model optimizing module for optimizing the signal recognition model according to the checking result;
The controller is connected with a second image feature acquisition module for carrying out feature acquisition on the detected image, the controller is connected with a recognition area judgment module for judging a recognition area in the detected image according to the image features, and the controller is connected with a recognition accuracy judgment module for judging the recognition accuracy of the optimized signal recognition model;
The controller is connected with a standard template acquisition module for acquiring standard images of the signal lamps, is connected with an identification area acquisition module for acquiring identification areas in detection images, and also comprises a comparison analysis module for comparing and analyzing the standard images of the signal lamps and the identification areas in the detection images, is connected with a color identification module for carrying out color identification on the identification areas in the detection images, and is connected with a comprehensive judgment module for comprehensively judging the states of the signal lamps according to the comprehensive color identification results of the comparison analysis results;
When the recognition accuracy judging module judges that the optimized signal recognition model has errors on the recognition result of the signal lamp state in the recognition area image, the controller starts the standard template collecting module, the recognition area collecting module and the color recognizing module;
The standard template acquisition module acquires standard images of all types of signal lamps from the standard image library, the identification area acquisition module receives the identification areas in the detection images sent by the identification area judgment module, the comparison analysis module analyzes and matches the identification areas in the detection images in the standard images of all types of signal lamps, and the matched standard images of the signal lamps are sent to the comprehensive judgment module;
the comprehensive judgment module is used for comprehensively judging the signal lamp state according to the comparison analysis result comprehensive color recognition result, and inputting the result obtained by comprehensive judgment of the signal lamp state and the corresponding detection image into the optimized signal recognition model for model training;
The signal lamp state recognition system comprises a controller, a signal lamp state recognition module, a recognition accuracy judgment module, a recognition result output module and a recognition result output module, wherein the signal lamp state recognition module is connected with the controller and used for outputting a signal lamp state recognition result, when the recognition accuracy judgment module judges that the recognition result of the signal recognition module is accurate, the recognition result output module directly outputs the recognition result of the signal recognition module, and otherwise, the recognition result output module outputs a comprehensive judgment result of the comprehensive judgment module on the signal lamp state.
2. The deep learning-based driving training traffic light detection system according to claim 1, wherein: the first image feature acquisition module receives the training image which is sent by the image preprocessing module and is preprocessed, and sends the image features which are extracted from the training image to the signal recognition model which is constructed by the recognition model construction module, and the recognition result checking module checks the recognition result of the signal recognition model.
3. The deep learning-based driving training traffic light detection system according to claim 2, wherein: the recognition result checking module receives the input result of the signal lamp state in the training image by a person and checks the input result with the recognition result of the signal recognition model;
When the recognition result checking module judges that the recognition result of the signal recognition model is inaccurate, the recognition model optimizing module optimizes the signal recognition model through a random gradient descent method.
4. The deep learning based driving training traffic light detection system according to claim 3, wherein: after the identification area judgment module judges the identification area in the detection image, the identification area image in the detection image is sent to an optimized signal identification model, and the signal identification model identifies the signal lamp state in the identification area image.
5. The deep learning based driving training traffic light detection system according to claim 2 or 4, wherein: the training image acquisition module acquires training images randomly from a training image library and sends the training images to the image preprocessing module, and the detection image acquisition module sends the acquired detection images containing signal lamps to the image preprocessing module.
6. The deep learning based driving training traffic light detection system according to claim 5, wherein: the image preprocessing module carries out image noise reduction and image enhancement processing on the training image and the detection image.
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