Night Vehicle Detection Algorithm Based on YOLOv5s and Bistable Stochastic Resonance

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.