Abstract: To address the issues of missed and false detections caused by overly dense distributions of small objects in the task of small object detection in drone aerial imagery,which can lead to mutual occlusion,a lightweight object detection method with multi-scale feature fusion is proposed.Firstly,a multi-scale occlusion module is introduced to enhance the network’s multi-scale information extraction capability,reduce semantic differences between different scales,and improve detection performance for occluded small objects.Secondly,a more efficient shared detection head strategy is proposed,which shares feature information of different scales through shared convolution across different detection heads,significantly reducing the model’s parameter count and achieving model lightweighting.Finally,a softened non-maximum suppression(NMS) method is introduced to solve the pro-blem of missed and false detections in dense occlusion scenarios caused by traditional greedy NMS,further improving detection accuracy.The effectiveness of the improved model is evaluated on the Visdrone-2019 and RSOD datasets,with the mean average precision(mAP) of the improved model increases by 9.0% and 6.0% respectively compared to the baseline model,and the model parameters reduce by 12.6%.Experimental results show that the improved algorithm can enhance the accuracy of object detection in drone aerial imagery while ensuring lightweighting,helping drone systems to identify and track targets more accurately,thereby improving the reliability and efficiency of task execution.

Key words: Drone aerial photography, Small object detection, Deep learning, Non-maximum suppression, YOLOv8