Abstract:A lightweight object detection network YOLOv5-tiny is given on the basis of YOLOv5 for real-time target detection tasks in drone-captured scenarios. The replacement of the original backbone network CSPDarknet53 with MobileNetv3 reduces the parameters of the network model and substantially improves the detection speed. Furthermore, the detection accuracy is improved by the introduction of the CBAM attention module and the SiLU activation function. With the characteristics of the aerial photography task dataset VisDrone, the anchor size is optimized, and data augmentation methods such as Mosaic and Gaussian blur are used to further improve the detection effect. Compared with the results of the YOLOv5-large network, the detection efficiency (FPS) is improved by 148% at the expense of a 17.4% reduction in mAP. Moreover, the network size is only 60% of that of YOLOv5 when the detection results are slightly superior.