ARF-YOLOv8: a novel real-time object detection model for UAV-captured images detection

Abstract

There are several difficulties in the task of object detection for Unmanned Aerial Vehicle (UAV) photography images, including the small size of objects, densely distributed objects, and diverse perspectives from which the objects are captured. To tackle these challenges, we proposed a real-time algorithm named adjusting overall receptive field enhancement YOLOv8 (ARF-YOLOv8) for object detection in UAV-captured images. Our approach begins with a comprehensive restructuring of the YOLOv8 network architecture. The primary objectives are to mitigate the loss of shallow-level information and establish an optimal model receptive field. Subsequently, we designed a bibranch fusion attention module based on Coordinate Attention which is seamlessly integrated into the detection network. This module combines features processed by Coordinate Attention module with shallow-level features, facilitating the extraction of multi-level feature information. Furthermore, recognizing the influence of target size on boundary box loss, we refine the boundary box loss function CIoU Loss employed in YOLOv8. Extensive experimentation conducted on the visdrone2019 dataset provides empirical evidence supporting the superior performance of ARF-YOLOv8. In comparison to YOLOv8, our method demonstrates a noteworthy 6.86% increase in mAP (0.5:0.95) while maintaining similar detection speeds. The code is available at https://github.com/sbzeng/ARF-YOLOv8-for-uav/tree/main.