Research On Traffic Target Recognition And Detection Based On Computer Vision

With the rapid increase of the number of motor vehicles and the rapid development of road traffic,how to ensure the safety of driving and reduce traffic accidents has become a concern.Therefore,the traffic target recognition and detection technology based on computer vision has important research value.This paper focuses on the recognition and detection of traffic targets.The main research contents are as follows:(1)In view of the problems of low recognition accuracy and robustness of traffic signs in traditional machine learning method and low recognition accuracy of traffic signs in classical convolution neural network method.This paper designs a multi-stage feature fusion-based convolutional neural network traffic sign recognition method,including iterative deep aggregation structure and hierarchical deep aggregation structure.This method effectively improves the feature extraction ability of traffic sign image,and use data enhancement and label smoothing methods to strengthen the model’s generalization ability.The experimental results show that the method in this paper has the characteristics of high precision and strong generalization ability.(2)Aiming at the problem of traffic target detection in automatic driving scene,this paper proposes an iterative aggregation high-resolution network anchor free traffic target detection method,which is improved on the basis of Center Net.Firstly,the highresolution representation backbone network is introduced.The high-resolution representation backbone network can maintain the resolution of the feature map in the process of feature extraction,and effectively reduce the loss of spatial semantic information in the process of image downsampling.The feature maps of different resolutions output by the high-resolution representation backbone network are fused by iterative aggregation method,making full use of the advantages of network extraction feature information.Finally,attention mechanism is used to optimize the feature information extracted from the model.The experimental results show proposed method has higher accuracy,meets the performance requirements of real-time detection and has good robustness.(3)Aiming at the problem that the current convolution model is too large and occupies too much storage,which is difficult to deploy on the computing platform with limited resources,this paper studies the traffic target detection method based on the lightweight convolution neural network Mobile Netv3.Mobile Netv3-Large is introduced as the encoder to extract features.The depth separable convolution used by Mobile Netv3-Large can effectively reduce the parameters of the model.The network structure of the decoder is analyzed and designed,which makes full use of the output features of the encoder and further improves the performance of the model with less parameters and computation.Experimental results show that the proposed method achieves a good balance in detection accuracy,model storage size and detection speed,and is an effective lightweight traffic target detection method.