Automatic driving target detection method based on improved YOLOv7
Aiming at the problem of misdetection and omission caused by dense vehicle targets,mutual occlusion and too small targets in automatic driving scenarios,a vehicle target detection algorithm with improved YOLOv7 is pro-posed.The ACmix hybrid attention mechanism is added after the SPPCSPC of the backbone network to fully mine the feature information,enhance the network's attention to the vehicle information,reduce the interference of other targets,and improve the detection accuracy;the Swin Transformer is added to the Neck end to collect the global information;the 160x160-size target detection head is added to increase the number and density of the anchors and to improve the network's ability to perceive small targets;finally,Soft-NMS flexible non-maximum suppression is utilized to reject redundant candidate frames and improve the leakage detection ability.The feasibility of the improvement is verified by experiments and compared with five mainstream networks,and the average accuracy reaches 91.5％,and compared with the basic network YOLOv7,the average accuracy is improved by 7.1％,and the operation speed reaches 105 FPS,which proves the effectiveness of the improved method.
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