Vehicle detection algorithm based on improved YOLOv3
Aiming at the problems of vehicle detection in the traffic scene such as a large number of small targets and severe target occlusion,a single-stage target detection algorithm based on the improved YOLOv3 is proposed.Since the small target only contains fewer pixels and features are not obvious,this algorithm builds a Soft-SPP structure based on the idea of spatial pyramid pooling,which integrates multiple receptive fields and adopts soft-pooling operation to retain details to the maximum extent and avoids information loss.The coordinate attention mechanism is introduced to capture the remote dependence with accurate location information.and adjust the weight assigned to each channel feature to make the network better learn important information.A loss function KIoU Loss based on key points and aspect ratio is proposed as the boundary box loss function,which makes the boundary box regression more accurate.The experimental results show that the mAP of the improved algorithm on the autopilot KITTI data set is 94.69％,which is 4.13％higher than that of the original YOLOv3 algorithm,and the detection speed is only reduced by 3.16 frame·s-1,which significantly improves the detection accuracy while maintaining the detection speed.
vehicle detectiondeep learningYOLOv3coordinate attentionsoft-SPPKIoU Loss
NETL
NSTL
万方数据
