A Lightweight Method for Small Object Detection Models on Unmanned Aerial Vehicles Based on L-FPN

Abstract

Oriented object detection in remote sensing images is a current research hotspot. Due to the varying 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 algorithms 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, remove redundant high-level feature maps in the FPN. Finally, adjust the anchor box sizes for small object detection. 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 inference speed.

Keywords

• |object detection|feature pyramid|model lightweight|remote sensing images|uav