现代电子技术2025，Vol.48Issue(9)：173-179,7.DOI:10.16652/j.issn.1004-373x.2025.09.026
改进Faster R-CNN的输电线路山火图像检测方法
Improved Faster R-CNN method for detecting wildfire images in transmission lines
摘要
Abstract
In view of the serious threat of wildfires to the safety of transmission lines,an improved Faster R-CNN image detection method for transmission line wildfires is proposed.The ResNeSt50 is selected as the backbone network to improve model performance,and the recursive feature pyramid(RFP)is added behind the backbone network to enhance the model's feature extraction ability at multiple scales.The CIoU Loss regression loss function is adopted to improve the bounding box regression rate and localization accuracy,and the Focal Loss classification loss function is used to improve the accuracy of smoke and flame detection for small objects.The Kmeans++clustering algorithm is used to optimize anchor size for smoke and flame data,so as to improve the detection accuracy of the algorithm.The data enhancement technology is used to eliminate the facts that insufficient images and weather environment changes will affect the detection accuracy.After training and testing,the results show that the improved Faster R-CNN method achieves a mean average precision(mAP)of 95.54%,which is 7.39%higher than that of the original model.To sum up,it can effectively identify smoke and flames generated near transmission lines,meeting the requirements of accuracy and real-time detection of wildfires.关键词
深度学习/山火检测/烟雾检测/Kmeans++/ResNeSt50/CIoU Loss/Focal Loss/RFPKey words
deep learning/wildfire detection/smoke detection/Kmeans++/ResNeSt50/CIoU Loss/Focal Loss/RFP分类
电子信息工程引用本文复制引用
黄力,吴珈承..改进Faster R-CNN的输电线路山火图像检测方法[J].现代电子技术,2025,48(9):173-179,7.基金项目
湖北省自然科学基金项目(2020CFB376) （2020CFB376）
