吉林大学学报（信息科学版）2023，Vol.41Issue(5)：801-809,9.
基于改进Yolov5的遥感光伏检测算法
Taget Detection of Photovoltatic Remote Sensing Based on Improved Yolov5 Model
摘要
Abstract
Aiming at high-sensing photovoltaic image resolution,high environmental noise,and complex background,an improved Yolov5 model is proposed to achieve positioning of photovoltaic power plants.First of all,the CA(Coordinate Attention)mechanism is added to the compassionate layer of the main feature extraction network to improve the learning ability of the network characteristics;second,the Ghostconv network structure is added to Backbone,useing the Ghostconv network module to replace the Conv network module,designing a new GhostC3 network network instead of the original C3 network module to improve the learning efficiency of the model;finally,the GIoU_Loss function is changed to the SIoU_Loss function.Compared with the original Yolov5 method,the average accuracy of the improved algorithm mAP,accuracy,and recall rate reached 97.5％,98.9％,and 94.9％,respectively,which have increased by 1.8％,1.7％,and 5.8％,respectively.The algorithm has a good effect on photovoltaic detection.关键词
光伏/遥感图像/目标检测/Yolov5模型Key words
photovoltaic/remote sensing images/target detection/Yolov5分类
信息技术与安全科学引用本文复制引用
佟喜峰,杜鑫,王志宝..基于改进Yolov5的遥感光伏检测算法[J].吉林大学学报（信息科学版）,2023,41(5):801-809,9.基金项目
黑龙江省自然科学基金资助项目(LH2021F004) （LH2021F004）
东北石油大学青年基金资助项目(HBHZX202002) （HBHZX202002）
东北石油大学研究生教育创新工程基金资助项目(JYCX_11_2020) （JYCX_11_2020）
