基于改进YOLOv7的输电铁塔塔基检测算法OA北大核心CSTPCD

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.