Abstract: At present, the target detection technology based on UAV aerial photography is widely used in military and civil fields, but the accuracy of target detection is not high because of the long imag- ing distance, blurred images taken at high altitudes, and small proportion of target information. To solve this problem, an improved algorithm based on YOLOv5 is proposed. Firstly, the original image is fogged to improve its robustness on foggy days. Secondly, the importance of different channels and spaces is enhanced through the integration of CBAM modules. Furthermore, the SPP in the original algorithm is replaced by the ASPP to reduce the influence of pooling operation on feature information. Finally, a detection head is added to the FPN structure to detect targets with finer granularity. Taking YOLOv5s as baseline, the experiment proves that the improved algorithm increases mAP_0.5 by 6.9% in comparison to the original algorithm, and can be effectively applied to the detection of small targets in aerial photography.

Key words: YOLOv5, unmanned aerial vehicle(UAV), attention mechanism, spatial pyramid pool- ing, feature pyramid