An Improved Small Object Detection Method in Remote Sensing Images Based on YOLOv8

Abstract

Small object detection has long been a difficulty and research hotspot in computer vision. Driven by deep learning, small object detection has made major breakthroughs and has been successfully used in fields such as national defense security, intelligent transportation, and industrial automation. In our research, we conduct a comprehensive analysis and improvement of the YOLOv8-n algorithm for object detection, focusing on the SE Attention and detection heads of small object. Through detailed ablation studies to assess its contribution to model performance, each strategy is systematically evaluated individually and collectively. The results show that each strategy uniquely enhances the performance of the model, significantly improving mAP when the two strategies are integrated.