Abstract:

UAV(Unmanned Aerial Vehicle) aerial photography, characterized by multi-angle, large field of view, and large-scale scenes, often results in images with numerous small objects, complex backgrounds, and difficult feature extraction. To address these issues, a new model, CA-NWD-YOLOV5 ( Coordinate Attention- Normalized Wasserstein Distance-You Only Look Once v5) is proposed. Based on the YOLOv5 model, a multi- scale detection layer is added to the head network to extract the features of small targets. It also incorporates a CA attention mechanism into the backbone network to prevent the model from overlooking target location

information. Lastly, the normalized Wasserstein distance loss function replaces the loss function based on intersection ratio, enhancing the model’s sensitivity to small targets. Experiments on the VisDrone2019 dataset demonstrate that, compared to the improved YOLOv5 model, the CA-NWD-YOLOv5 model can effectively enhance the detection accuracy of small and medium-sized targets in UAV aerial photography images. The mAP_ 0. 5 of the improved algorithm reaches 50% , proving its effective application to the detection of small targets in aerial photography.

Key words: aerial images, small target detection, attention mechanisms, Wasserstein distance