A Lightweight Method for Small Object Detection Models on Unmanned Aerial Vehicles Based on L-FPN
Oriented object detection in remote sensing images is a current research hotspot.Due to the var-ying heights and equipment used in capturing remote sensing images,the ground sampling distance(GSD)of each image also varies,causing many small objects to be easily overlooked.Existing rotated object detection al-gorithms are mainly aimed at multi-scale object detection in general scenarios.The feature pyramid network(FPN)has complex and time-consuming fusion computations,which still faces great challenges when deployed on edge devices like UAVs.Therefore,this paper proposes a lightweight method for small object detection in UAVs based on L-FPN.First,normalize the scale according to the GSD information of the image.Second,re-move redundant high-level feature maps in the FPN.Finally,adjust the anchor box sizes for small object detec-tion.The method is trained and validated on the DOTA dataset.Results show that compared to the traditional models,the proposed L-FPN-based lightweight method for small object detection in UAVs achieves consistent recognition accuracy,with 2.7%fewer model parameters,28%smaller model size,and 13.24%faster infer-ence speed.
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