RE-YOLO: a lightweight small object detection method for UAV remote sensing imagery

Abstract

To address the challenges of accurately extracting target features from complex scenes in UAV remote sensing imagery and the susceptibility of small objects to being obscured by noise, this paper proposes a lightweight detection algorithm, RE-YOLO, based on YOLOv8n. First, a multi-scale convolutional module named RFCSConv, which integrates channel and spatial attention mechanisms based on Receptive Field Attention Convolution (RFAConv), replaces the original convolution layers. This enhances feature selection and fusion at multiple scales. Second, the Efficient Squeeze-and-Excitation Module (ESEModule) is introduced into the backbone to strengthen feature representation while reducing computational overhead. Lastly, a composite loss function called Win-IoU, combining Wise-IoU (WIoU) and Inner-IoU, is proposed to dynamically adjust gradient contributions based on anchor quality. Experimental results on the VisDrone2019 dataset demonstrate that RE-YOLO achieves 29.7% mAP@0.5 with only 3.2MB of parameters and a real-time speed of 150 FPS. The algorithm also generalizes well across the HRSID and CARPK datasets, achieving 91.8% and 94.3% mAP@0.5 respectively.