Research on remote sensing small object detection algorithm based on improved YOLOv8

Abstract

Remote sensing target detection is a component of the computer vision field, with broad applications in natural disaster early warning and military operations. This study focuses on improving YOLOv8 network structure and proposes a small target detection network, YOLOv8-RSG, which incorporates multi-scale feature fusion. The dataset is augmented using the Mosaic data augmentation method to enhance small target detection capabilities. To address the issue of suboptimal detection performance in densely populated small target areas in remote sensing images, an incentive weight factor is introduced into the confidence loss function to optimize the function. Additionally, WIOU is utilized as the bounding box loss function to improve regression accuracy. In this experiment, YOLOv8-RSG demonstrates an average precision improvement of 5.5% and 6.0% in mAP0.5 and mAP0.5:0.95, respectively, compared to YOLOv8-base. The proposed YOLOv8-RSG model effectively achieves small target detection, providing theoretical and technical support for visual research in remote sensing scenarios.