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

输电塔作为整个电力传输系统最重要的组成部分之一,需要及时对输电塔进行检测保证塔基的稳固以保障后期的使用。针对无人机采集到的输电塔图像存在背景复杂、背景与目标塔基对比度低、小目标及塔基不完整等问题,提出了基于改进YOLOv7的输电塔塔基检测算法。首先,通过无人机采集不同地形地貌的输电塔图像,构建高质量数据集。然后,在原始YOLOv7的Backbone层中加入卷积注意力模块CBAM注意力机制,以提高输电塔塔基特征的提取能力。最后,引入WIoU v3代替原坐标损失函数CIoU,以提高目标检测任务的准确性和稳定性。在该数据集上,使用改进后的YOLOv7算法与目前主流的目标检测算法进行对比实验,实验结果中所提算法的mAP值高达99.93%,比原始YOLOv7提高2.19%,FPS值为37.125,满足实时检测需求,算法的整体性能较好。实验验证了所提算法在塔基检测上的可行性和有效性,为后续塔基区周围水土情况的研究奠定了基础。

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.