Vehicle Detection Method Based on Improved YOLOv5s in Foggy Scene

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Vehicle Detection Method Based on Improved YOLOv5s in Foggy Scene

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[1]YUAN Laohu,CHANG Yukun,LIU Jiafu.Vehicle Detection Method Based on Improved YOLOv5s in Foggy Scene[J].Journal of Zhengzhou University (Engineering Science),2023,44(03):37-43.[doi:10.13705/j.issn.1671-6833.2023.03.005]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 44 Number of periods: 2023 03 Page number: 37-43 Column: Public date: 2023-04-30

Title:: Vehicle Detection Method Based on Improved YOLOv5s in Foggy Scene

Author(s):: YUAN Laohu; CHANG Yukun; LIU Jiafu; Shenyang University of Aeronautics and Astronautics University of Aeronautics and Astronautics, Liaoning Shenyang 110136

Keywords:: deep learning; YOLOv5s; vehicle detection; data augmentation; fog simulation

CLC:: TP391

DOI:: 10.13705/j.issn.1671-6833.2023.03.005

Abstract:: In order to solve the problems of low recognition accuracy and easy omission of existing target detection methods in foggy scenes, an improved vehicle detection method based on YOLOv5s was proposed. Firstly, based on VisDrone data set, LightFogVisDrone and ThickFogVisDrone were generated by atmospheric scattering model,and the MixFogData was composed of real fog scene pictures. Secondly, the Mosaic data enhancement method of the original model was improved from the original 4 pictures to 9 pictures randomly, which reduced the gray background area, accelerated the convergence of the model and improved the training efficiency, and the CBAM attention mechanism module was added before the prediction end to improve the feature extraction ability of the network to tackle the problem of missed detection of occluded targets and small targets. Finally, the prior frame of NMS non-maximum suppression value was optimized to improve the problem of missing detection of vehicle targets. The experimental results showed that, compared with the original YOLOv5s, the average accuracy of the improved YOLOv5s in light fog, dense fog and mixed fog was increased by 16.14, 16.16 and 15.05 percentage points, respectively, which proved that the improved YOLOv5s was effective and practical for vehicle target detection in foggy environment.

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Last Update: 2023-05-08