Efficient target detection system based on deep learning
Through an efficient object detection system based on deep learning, using technologies such as Faster R-CNN architecture and Anchor mechanism, the accuracy and speed problems of traditional vehicle detection methods in complex environments are solved, and efficient and accurate vehicle detection is achieved, suitable for intelligence In areas such as transportation and autonomous driving.
Patent Information
- Application Number
- CN202411423371.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-10-12
- Publication Date
- 2025-01-14
AI Technical Summary
Traditional vehicle detection methods have low detection accuracy, slow processing speed and poor adaptability in complex visual information and variable environments, making it difficult to meet the actual needs of intelligent transportation and autonomous driving.
Design an efficient object detection system based on deep learning, including data acquisition, preprocessing, model training and object detection modules, using Faster R-CNN architecture, Anchor mechanism, RoIPooling layer and multi-scale training, use deep learning algorithms to extract image features and generate Candidate areas are classified and regressed to achieve accurate detection of vehicle location and category.
It realizes high-precision vehicle detection in complex visual information and changing environments, provides fast processing speed and powerful adaptability, and is suitable for intelligent transportation, autonomous driving and video surveillance, and can monitor traffic conditions in real time and provide reliable vehicles. Test results.
Smart Images
Figure CN119313877A_ABST
Abstract
Description
Technical Field
[0001] The present invention belongs to the technical field of target detection, and in particular relates to an efficient target detection system based on deep learning. Background Art
[0002] With the development of society and the advancement of science and technology, the demand for vehicle detection in fields such as intelligent transportation, autonomous driving and video surveillance is growing;
[0003] Traditional image processing methods usually use edge detection, morphological processing and other technologies to pre-process the image, and then detect vehicles through template matching, feature extraction and other methods. They have relatively low computational complexity and can achieve a certain degree of vehicle detection in some simple scenarios.
[0004] Although traditional vehicle detection methods can achieve a certain degree of vehicle detection in some simple scenarios, they have problems such as low detection accuracy, slow processing speed, and poor adaptability when faced with complex visual information and various environmental conditions, making it difficult to meet the needs of practical applications.
[0005] To this end, an efficient target detection system based on deep learning is designed to solve the above problems. Summary of the invention
[0006] To solve the problems raised in the above background technology, the present invention provides an efficient target detection system based on deep learning, which can effectively solve the problems raised in the above background technology.
[0007] To achieve the above object, the present invention provides the following technical solution: an efficient target detection system based on deep learning, comprising:
[0008] A data acquisition module, used for acquiring image data;
[0009] A data preprocessing module performs preprocessing operations on the collected images;
[0010] Model training module, builds a network based on the Faster R-CNN architecture, selects a pre-trained model as the basic convolutional network, uses the labeled vehicle detection dataset for training, and uses an optimization algorithm to adjust the network parameters;
[0011] The object detection module loads the trained model, extracts image features through the basic convolutional network, generates candidate regions using the region proposal network, and then uses the Fast R-CNN detector to classify and regress the candidate regions to determine the vehicle location and category;
[0012] The result output module is used to output the vehicle detection results.
[0013] As a preferred embodiment of the efficient target detection system based on deep learning of the present invention, the data acquisition module can acquire images from multiple data sources such as cameras and surveillance videos.
[0014] As a preferred embodiment of the efficient target detection system based on deep learning of the present invention, the data preprocessing module includes operations such as denoising, enhancement, and normalization to improve image quality.
[0015] As a preferred embodiment of the efficient target detection system based on deep learning of the present invention, the model training module adopts optimization algorithms such as stochastic gradient descent during the training process, and can use techniques such as early stopping and learning rate decay to prevent overfitting.
[0016] As a preferred embodiment of the efficient target detection system based on deep learning of the present invention, the target detection module adopts the Anchor mechanism to predefine a group of rectangular boxes of different sizes and proportions as candidate areas that may contain targets.
[0017] As a preferred embodiment of the efficient target detection system based on deep learning of the present invention, a RoIPooling layer is used in the FastR-CNN detector of the target detection module to convert candidate regions of different sizes into feature maps of fixed size.
[0018] As a preferred embodiment of the efficient target detection system based on deep learning of the present invention, it also includes a multi-scale training and detection method, in which the input image size is randomly adjusted during the training process, and images of different scales are detected and the results are fused during detection.
[0019] As a preferred embodiment of the efficient target detection system based on deep learning of the present invention, the result output module can display the location and category information of the vehicle in a visual manner, or output the detection results to other systems for further processing.
[0020] Compared with the prior art, the present invention has the following beneficial effects:
[0021] Used to automatically identify and locate vehicles in images, this system uses deep learning algorithms to extract features from images by training neural network models and achieve accurate detection of vehicles. Its key advantage is that it can handle complex visual information and vehicle detection tasks under various environmental conditions. The efficiency of deep learning models is reflected in their high detection accuracy and fast processing speed. They can monitor traffic conditions in real time and provide reliable vehicle detection results. Such systems are widely used in fields such as intelligent transportation, autonomous driving, and video surveillance. Deep learning algorithms have powerful feature extraction and generalization capabilities, and can automatically learn features in images to achieve accurate detection of vehicles. BRIEF DESCRIPTION OF THE DRAWINGS
[0022] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:
[0023] Figure 1 is a schematic block diagram of a system of the present invention; DETAILED DESCRIPTION
[0024] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.
[0025] Example: Figure 1 As shown, the present invention provides a technical solution, an efficient target detection system based on deep learning, including:
[0026] A data acquisition module, used for acquiring image data;
[0027] A data preprocessing module performs preprocessing operations on the collected images;
[0028] Model training module, builds a network based on the Faster R-CNN architecture, selects a pre-trained model as the basic convolutional network, uses the labeled vehicle detection dataset for training, and uses an optimization algorithm to adjust the network parameters;
[0029] The object detection module loads the trained model, extracts image features through the basic convolutional network, generates candidate regions using the region proposal network, and then uses the Fast R-CNN detector to classify and regress the candidate regions to determine the vehicle location and category;
[0030] The result output module is used to output the vehicle detection results.
[0031] Going further:
[0032] In an optional embodiment, the data acquisition module can acquire images from multiple data sources such as cameras and surveillance videos.
[0033] In this implementation scheme: using cameras, surveillance videos and other equipment to collect image data, vehicle images in different scenarios can be collected, such as highways, urban roads, parking lots, etc.
[0034] Going further:
[0035] In an optional embodiment, the data preprocessing module includes operations such as denoising, enhancement, and normalization to improve image quality.
[0036] In this embodiment: the collected image is preprocessed, including denoising, enhancement, normalization, etc. Image enhancement algorithms, such as histogram equalization and contrast enhancement, can be used to improve the quality of the image.
[0037] Going further:
[0038] In an optional embodiment, the model training module uses optimization algorithms such as stochastic gradient descent during the training process, and can use techniques such as early stopping and learning rate decay to prevent overfitting.
[0039] In this implementation scheme: construct the Faster R-CNN network architecture and select a suitable pre-trained model as the basic convolutional network.
[0040] The network is trained using a labeled vehicle detection dataset, and optimization algorithms such as stochastic gradient descent are used to adjust the network parameters to minimize the loss function.
[0041] During the training process, you can use techniques such as early stopping and learning rate decay to prevent overfitting and improve model performance.
[0042] Going further:
[0043] In an optional embodiment, the target detection module adopts the Anchor mechanism to predefine a set of rectangular boxes of different sizes and proportions as candidate regions that may contain targets, and the RoIPooling layer is used in the Fast R-CNN detector of the target detection module to convert candidate regions of different sizes into feature maps of fixed size.
[0044] In this implementation scheme: the trained model is loaded and vehicle detection is performed on the input image.
[0045] First, the image features are extracted through the basic convolutional network, then the RPN is used to generate candidate regions, and finally the Fast R-CNN detector classifies and regresses the candidate regions to determine the location and category of the vehicle.
[0046] It should be noted that: Anchor mechanism:
[0047] The Anchor mechanism is used in RPN to predefine a set of rectangular boxes of different sizes and proportions as candidate regions that may contain targets.
[0048] By adjusting the size and scale of the Anchor, it can accommodate vehicle targets of different shapes and sizes.
[0049] RoIPooling:
[0050] In the Fast R-CNN detector, a RoIPooling layer is used to convert candidate regions of different sizes into feature maps of fixed size for subsequent classification and regression operations.
[0051] RoIPooling can improve the efficiency and accuracy of the model and reduce the amount of calculation.
[0052] Multi-scale training and detection:
[0053] In order to improve the model's detection ability for vehicles of different sizes, a multi-scale training and detection method is adopted.
[0054] During the training process, the size of the input image is randomly adjusted so that the model can learn features of different scales. During detection, images of different scales are detected and the results are fused to improve the accuracy of detection.
[0055] Going further:
[0056] In an optional embodiment, the result output module can display the location and category information of the vehicle in a visual manner, or output the detection results to other systems for further processing.
[0057] In this implementation scheme: the detection results are displayed to the user in a visual manner, such as marking the location and category of the vehicle on the image.
[0058] The statistical information of the detection results can be output, such as the number of detected vehicles, location distribution, etc.
[0059] It should be noted that:
[0060] Applied in intelligent transportation systems: used for traffic flow monitoring, vehicle violation detection, road congestion analysis, etc. It can monitor the vehicle conditions on the road in real time and provide decision support for traffic management departments.
[0061] Application in autonomous driving: Helps autonomous driving vehicles identify surrounding vehicles and obstacles, achieve autonomous navigation and obstacle avoidance, and improve the safety and reliability of autonomous driving.
[0062] Applied to video surveillance: Real-time detection and tracking of vehicles in surveillance videos to improve security levels. It can be used in parking lot management, community security monitoring and other fields.
[0063] The Faster R-CNN architecture has high detection accuracy and processing speed, which can meet the needs of real-time applications.
[0064] Through data enhancement and transfer learning techniques, training time and computing resource consumption can be reduced.
[0065] The Anchor mechanism and RoIPooling technologies are used to improve the model's accuracy in positioning and classifying vehicle targets.
[0066] Multi-scale training and detection methods can adapt to vehicle targets of different sizes and improve the robustness of detection.
[0067] This system can be easily extended to other object detection tasks, such as pedestrian detection, traffic sign detection, etc.
[0068] The parameters and architecture of the model can be adjusted according to different application scenarios and requirements to improve the adaptability of the system.
[0069] With the continuous development and application of deep learning technology, efficient target detection systems based on deep learning will play an increasingly important role in the fields of intelligent transportation, autonomous driving, and video surveillance. In the future, we can further optimize the performance of the system, improve detection accuracy and processing speed, expand application scenarios, and provide users with better services. At the same time, we can also explore integration with other technologies, such as the Internet of Things and big data, to make greater contributions to the realization of intelligent transportation and smart cities;
[0070] Used to automatically identify and locate vehicles in images, this system uses deep learning algorithms to extract features from images and accurately detect vehicles by training neural network models. Its key advantage is that it can handle complex visual information and vehicle detection tasks under various environmental conditions. The efficiency of deep learning models is reflected in their high detection accuracy and fast processing speed, which can monitor traffic conditions in real time and provide reliable vehicle detection results. Such systems are widely used in fields such as intelligent transportation, autonomous driving, and video surveillance.
[0071] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.
Claims
1. An efficient target detection system based on deep learning, characterized by: include, A data acquisition module, used for acquiring image data; A data preprocessing module performs preprocessing operations on the collected images; Model training module, builds a network based on the Faster R-CNN architecture, selects a pre-trained model as the basic convolutional network, uses the labeled vehicle detection dataset for training, and uses an optimization algorithm to adjust the network parameters; The object detection module loads the trained model, extracts image features through the basic convolutional network, generates candidate regions using the region proposal network, and then uses the Fast R-CNN detector to classify and regress the candidate regions to determine the vehicle location and category; The result output module is used to output the vehicle detection results.
2. The efficient target detection system based on deep learning according to claim 1, characterized in that: The data acquisition module can acquire images from multiple data sources such as cameras and surveillance videos.
3. The efficient target detection system based on deep learning according to claim 1, characterized in that: The data preprocessing module includes operations such as denoising, enhancement, and normalization to improve image quality.
4. The efficient target detection system based on deep learning according to claim 1, characterized in that: The model training module adopts optimization algorithms such as stochastic gradient descent during the training process, and can use techniques such as early stopping and learning rate decay to prevent overfitting.
5. The efficient target detection system based on deep learning according to claim 1, characterized in that: The target detection module adopts the Anchor mechanism to predefine a set of rectangular boxes of different sizes and proportions as candidate areas that may contain targets.
6. The efficient target detection system based on deep learning according to claim 1, characterized in that: The Fast R-CNN detector of the object detection module uses the RoI Pooling layer to convert candidate regions of different sizes into feature maps of fixed size.
7. The efficient target detection system based on deep learning according to claim 1, characterized in that: It also includes multi-scale training and detection methods. The input image size is randomly adjusted during the training process, and images of different scales are detected and the results are fused during detection.
8. The efficient target detection system based on deep learning according to claim 1, characterized in that: The result output module can display the location and category information of the vehicle in a visual manner, or output the detection results to other systems for further processing.