可再生能源2025，Vol.43Issue(12)：1638-1646,9.
一种针对小尺度风车的目标检测算法
An object detection algorithm for small-scale windmills
摘要
Abstract
Windmills are important facilities that convert wind energy into electrical energy.This paper addresses the issues of missed detections and false detections in the process of identifying small-scale windmills in remote sensing images by constructing a new GF-2 windmill dataset.It proposes the YOLO-WM algorithm for the small target detection task of windmills,adding a tiny object detection layer based on the YOLOv8 object detection algorithm,and incorporates a BSAM dynamic attention mechanism module in the backbone section.It replaces the C2f module with the lightweight C2f_RepGhost module and finally utilizes the SAHI slicing algorithm in the prediction stage to complete the windmill target detection task.Experimental results show that the YOLO-WM algorithm can better retain shallow convolutional features while reducing algorithm complexity,allowing for more accurate capture of small-scale target information.This method compensates for the shortcomings of small object detection datasets,and it has been verified that the improved algorithm performs better in detecting small-scale windmills than other algorithms.This research provides valuable insights for the rational optimization and allocation of wind energy resources and the joint analysis of geographical elements.关键词
风车/深度学习/小目标检测/YOLOv8/高分影像Key words
windmills/deep learning/small target detection/YOLOv8/GF images分类
能源科技引用本文复制引用
鲍熠琳,胡晋山,康建荣,朱益虎,王胜利..一种针对小尺度风车的目标检测算法[J].可再生能源,2025,43(12):1638-1646,9.基金项目
国家自然科学基金项目(52074133,41671395). （52074133,41671395）
