Small Target Detection Algorithm for Lightweight UAV Aerial Photography Based on YOLOv5

A lightweight unmanned aerial vehicle (UAV) aerial photography small target detection algorithm GA-YOLO based on YOLOv5 is proposed to address the problem of small target feature size, complex background, and dense distribution in images from the perspective of UAV aerial photography. This algorithm improves the Mosaic data augmentation method and overall network structure, and adds a small object detection head. At the same time, a lightweight global attention module and a parallel spatial channel attention mechanism module are designed to enhance the network's global feature extraction ability and the competition and cooperation between convolutional channels during the training process. Based on the 4.0 version of YOLOv5s, experiments were conducted on the publicly available drone aerial photography dataset VisDrone2019-DET. The results showed that the improved model reduced the number of parameters by 48% and the computational complexity by 26% compared to the original model, and mAP@0.5 improved by 4.9 percentage points, mAP@0.5 0.95 increased by 3.3 percentage points, effectively enhancing the detection capability of unmanned aerial vehicles for dense small targets from an aerial perspective.