陕西师范大学学报（自然科学版）2024，Vol.52Issue(3)：85-95,11.DOI:10.15983/j.cnki.jsnu.2024012
基于改进YOLOv7的输电铁塔塔基检测算法
A novel algorithm based on the improved YOLOv7 for detecting transmission tower base
摘要
Abstract
The pylon is one of the most important components in the entire power transmission system.It is necessary to timely inspect the tower to ensure the stability of the base for the later use.There are problems of the transmission tower images collected by UAV have complex backgrounds,the background is similar to the base of target tower,as well as small objects and incomplete tower base,this paper proposes an improved YOLOv7 algorithm for detecting the base of tower.Firstly,using the pylon images of different landforms to construct high-quality data sets.Then CBAM attention mechanism is added to the Backbone layer of the original YOLOv7 to improve the feature extraction ability of the pylon.Finally,introducing WIoU v3 instead of the original coordinate loss function CIoU to improve the veracity and stability of target detection tasks.On this dataset,a comparative experiment was conducted using the improved YOLOv7 algorithm and the current mainstream object detection algorithm.The mAP value of our algorithm is as high as 99.93%in the experimental results,it is 2.19%higher than the original YOLOv7,the FPS value is 37.125,which meets the real-time detection requirements,and the overall performance of the algorithm is good.It's feasible and effective in detection tasks of towers'base for our algorithm,which has been proven by the experiments in this paper,and laying the foundation for future research on the soil and water around the base of tower.关键词
输电塔塔基/YOLOv7/目标检测/卷积块注意力模块/WIoU v3Key words
transmission tower base/YOLOv7/object detection/convolutional block attention module(CBAM)/WIoU v3分类
信息技术与安全科学引用本文复制引用
雷磊,魏小龙,梁俊,董倩,肖樟树..基于改进YOLOv7的输电铁塔塔基检测算法[J].陕西师范大学学报（自然科学版）,2024,52(3):85-95,11.基金项目
陕北地区电网工程水土流失及次生灾害风险识别与治理关键技术研究与应用(5226KY22000K) （5226KY22000K）
国家自然科学基金(61672333) （61672333）
