Aiming at the problem that low-altitude micro-UAVs pose a threat to public safety, this paper proposes a lightweight target detection model YOLOv5_SS suitable for mobile terminals based on the you only look once v5 (YOLOv5) network. In this model, the lightweight network ShuffleNetv2 replaces the original backbone network of YOLOv5, introduces squeeze-and-excitation networks (SENet) attention mechanism, and uses soft non-maximum suppression (Soft-NMS) algorithm to improve the detection effect of dense overlapping targets. The experimental results show that the mean average precision@0.5 (mAP50) of the model for the detection of low-altitude micro-UAV on the dataset is 92.75%, the accuracy is 90.49%, and the number of parameters is 0.237 4 M. The number of floating-point operations is 0.9GFLOPS (giga floating-point operations).