Abstract:Aiming at the needs of air-to-earth observation weak and small target recognition and tracking technology, an improved multi-target recognition and detection method of YOLOv5m network is proposed to improve the recognition ability of weak and small targets with less than 10*10 pixels; The influence of Mosaic data enhancement, Anchor calculation, Focus module and SPP module on weak and small targets at the input end of the network structure is analyzed; In the Prediction layer of the deep learning network, the distance intersection over union non-maximum suppression (DIoU-NMS) is introduced to replace the traditional non-maximum suppression (NMS), and the distance intersection over union loss function (DIoU_Loss) is introduced to replace the generalized intersection over union loss function (GIoU_Loss), speed up the bounding box regression rate, improve the positioning accuracy, eliminate overlapping detection, and introduce a target recognition layer with more than 4*4 pixels in the network to improve the accuracy of occlusion overlapping weak and small targets; The experimental results show that, compared with the classic YOLOv5m network, the improved deep learning network algorithm achieves an average average precision mAP index of 89.7%, which is 4.1% higher than the original network, and realizes the image pixel number less than 10*10. The high-precision identification of weak and small targets effectively improves the adaptability and application value of the deep learning network to weak and small targets.
