Pedestrian Detection Method for Autonomous Driving Vehicles Based on YOLOv5s
An enhanced YOLOv5s model is proposed to improve pedestrian detection accuracy in autonomous driving applications.This enhancement includes expanding the original three-scale detection head to a four-scale detection structure by adding an additional layer specialized in small object detection,thereby augmenting the model's capability to detect small-scale objects.In the neck network,a Coordinate Attention(CA)mechanism is integrated into the C3 module,forming a C3-CA module to reinforce spatial dependencies between features,which enables more precise pedestrian localization.Furthermore,the original CIoU loss function is replaced with an EIoU loss function to enhance model convergence.Experiments conducted on the BDD100K dataset demonstrate that the improved model,compared with the baseline YOLOv5s,achieves 1.7%increase in detection precision,4.8%boost in recall,and 2.2%improvement in mean average precision,effectively reducing missed and false detections.
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