Research On Fine-grained Vehicle Recognition System Based On Convolutional Neural Network

Under the background of the profound development of urbanization,and the development of economy and industry,the popularity rate and retention of vehicle have increased rapidly,and the numerous problems that followed have become major challenges for government.In order to relieve urban traffic pressure,shorten the length of obstruction,reduce the frequency of accidents,and improve air quality,it is necessary to establish an Intelligent Transportation System(ITS)of people,vehicles,and roads.ITS integrates many leading disciplines such as information,communication,big data analysis,computer vision and so on.This thesis designes and implements a vehicle identification system based on convolutional neural network.The system integrates multiple technologies such as moving object detection,fine-grained image recognition,multi-task learning and distributed architecture to achieve multi-attribute identification of video vehicles.Traffic Management provides basic property information for road vehicles.The main content of this thesis is as follows:The first two chapters briefly explain the background and significance of this topic,relevant theories and key technologies.The third chapter discusses the method of single-task learning by splitting sub-tasks separately under the requirements of multi-attribute recognition for fine-grained vehicles.It not only consumes time and costs,but also ignores the internal relations among sub-tasks.To solve the bottleneck encountered by single-task learning,this thesis proposes a method for fine-grained vehicle attribute recognition based on multi-task convolutional neural network.Firstly,This method designs a multi-task convolutional neural network for fine-grained vehicle attributes,then increases the similarity constraint and adopts a combination.Optimized way to realize fine-grained vehicle multi-attribute recognition.The simulation results show that the multitask convolutional neural network not only reduces the training time for complex tasks,but also considers the correlation between various attributes,which improves the recognition accuracy to some extent.The fourth chapter describes the specific design and implementation process of vehicle identification system based on convolutional neural network.Firstly,from the analysis of system requirements and the analysis of system function requirements,the overall architecture and various functional modules of the system are designed,and the workflow and business process of the system are designed.Then in accordance with the various functional modules from the implementation,interface and algorithm support point of view detailed description of the module's implementation.In particular,the "ghost area" that occurs in the detection of sports vehicles is causing a waste of system resources.Using the histogram matching method,the phenomenon of "ghost area" is well suppressed,and the utilization of resources of the system is improved.Through the system test system,the detection and recognition of the vehicles in the surveillance video are completed,and the real-time requirements for traffic monitoring are met,which has practical application value.