基于 YOLOv5 的增强多尺度目标检测方法

To address the problem that the initial anchor box is difficult to match the target and its multiscale detection ability is not strong in complex scenes, an enhanced multi-scale target detection method based on YOLOv5 is proposed. Through the Kmeans + + clustering algorithm, the multi-scale initialization anchors suitable for the current detection scene is obtained, which makes it easier for the network to capture targets with different scales; then, a number of parallel convolution branches with different scales are added to the Bottleneck structure. While retaining the original feature information, the multi-scale feature information is fused to enhance the global perception ability of the model. The EMYOLOv5s model proposed is tested on VisDrone2019, COCO2017, and PASCAL VOC2012 datasets. The experimental results show that: compared with the YOLOv5s model, the key indicators such as mAP@ 0. 5: 0. 95 and mAP @ 0. 5 are improved; on PASCAL VOC2012, mAP @ 0. 5: 0. 95 is increased by 5. 2%, while the detection time is only increased by 1. 9 ms, indicating that EM-YOLOv5 model can effectively improve the target detection accuracy in general complex scenes.