Computer Science

Computer Science ›› 2024, Vol. 51 ›› Issue (9): 173-181.doi: 10.11896/jsjkx.230600056

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Night Vehicle Detection Algorithm Based on YOLOv5s and Bistable Stochastic Resonance

HU Pengfei1, WANG Youguo1,2, ZHAI Qiqing1, YAN Jun2, BAI Quan1   

  1. 1 School of Science,Nanjing University of Posts and Telecommunications,Nanjing 210023,China
    2 School of Communication and Information Engineering,Nanjing University of Posts and Telecommunications,Nanjing 210023,China
  • Received:2023-06-07 Revised:2023-11-15 Online:2024-09-15 Published:2024-09-10
  • About author:HU Pengfei,born in 1999,postgra-duate.His main research interests include stochastic resonance and deep learning-based vehicle detection.
    WANG Youguo,born in 1968,Ph.D,professor,Ph.D supervisor.His main research interests include signal and information processing,social network information dissemination and control,stochastic resonance theory and application.
  • Supported by:
    National Natural Science Foundation of China(62071248).

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Abstract: Aiming at the problems of missed and false detection caused by weak illumination during night vehicle detection,an improved night vehicle detection algorithm is proposed based on bistable stochastic resonance and YOLOv5s.YOLOv5s is improved from four aspects,replacing small structures in Backbone and Neck to improve the detection ability of the network to small targets.A dual attention mechanism composed of coordinate attention CA and energy attention SimAM is added to improve the feature extraction ability of the network to the target.The lightweight backbone Fasternet is adopted to reduce the amount of model parameters.The WIoU loss function is used in Head to accelerate the convergence speed of bounding box regression loss.The effectiveness of the nighttime vehicle dataset is analyzed from quantitative and qualitative perspectives by using classical bistable stochastic resonance,and the enhanced nighttime vehicle images are passed into the improved YOLOv5s network for training.Experimental results show that,compared with the original YOLOv5s,the night vehicle detection algorithm combining improved YOLOv5s and bistable stochastic resonance has better accuracy and lower missed detection rate when performing long-range small targets and densely occluded night vehicle detection tasks.

Key words: Bistable stochastic resonance, Low-light image enhancement, YOLOv5s, Dual attention mechanism, Lightweight backbone

CLC Number: 

  • TP391
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