Research on vehicle detection in foggy weather based on improved YOLOv5 algorithm

Meng Dai; Xujin Dong

doi:10.1117/12.2641045

2 December 2022 Research on vehicle detection in foggy weather based on improved YOLOv5 algorithm

Meng Dai, Xujin Dong

Proceedings Volume 12288, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2022); 122881Q (2022) https://doi.org/10.1117/12.2641045
Event: International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2022), 2022, Zhuhai, China

Abstract

In order to reduce the probability of vehicle rear end collision accident in foggy weather, and in accordance with the background of accelerating the development of automobile safety industry with artificial intelligence technology, a vehicle recognition, detection and voice early warning based on improved yolov5 algorithm is proposed. Firstly, adaptive convolution is introduced into convolution neural network to enhance the feature extraction ability of target in the process of training network model, and then improve the expression ability of neural network for important feature channels, especially for small target recognition and detection; Then, considering the real-time nature of detection and early warning, the neck detection layer in yolov5s network is cut and optimized to improve the reasoning speed of the network model. Finally, through the comparative analysis of the evaluation indexes such as the accuracy, recall and map of the training model, and the test of the scene pictures of the actual driver's perspective in foggy weather, it is verified that the improved yolov5 algorithm can improve the detection ability and reasoning speed of small target vehicles. The experimental results show that the accuracy of the improved yolov5 algorithm is as high as 97% and the reasoning speed is improved by 1/3. The driver can be warned in real time to reduce the probability of rear end collision accidents.

Citation Download Citation

Meng Dai and Xujin Dong "Research on vehicle detection in foggy weather based on improved YOLOv5 algorithm", Proc. SPIE 12288, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2022), 122881Q (2 December 2022); https://doi.org/10.1117/12.2641045

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KEYWORDS

Detection and tracking algorithms

Convolution

Target detection

Neck

Evolutionary algorithms

Target recognition

Algorithm development

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