基于人工智能图像识别的输电线路巡检研究

为了准确、及时地发现输电线路中的缺陷，研究基于人工智能图像识别技术的输电线路立体化巡检模式。具体地，以人工智能图像识别技术为支持，借助K-means算法对立体巡检图像进行聚类处理，同时，采用人工神经网络对图像中输电线路缺陷进行智能识别。经测试，在相同工作量下，未采用本文所提技术的输电线路缺陷识别需要5个分析员持续工作15 d,平均每分钟进行2～3张图片的识别，图像识别速度为20～30 s/张；采用人工智能识别技术，识别速度高达0.25 s/张，仅需3.6 h便可以将识别任务完成。在弱光环境下，经过增强处理的图像边缘更加清晰，目标图像与背景实现明显分界，且现阶段可利用输电线路中相同部件不同角度的7张图片实现高于90%的识别准确率。另外，通过对相同条件下其他几种方法影响模型实际收敛情况的比较发现，所有方法的重构误差均呈现出逐渐降低之势，最终都趋于稳定。结果表明本文技术在立体化线路图像缺陷检测中有一定普适性，有利于工作效率的显著提升。

Research on transmission line inspection based on artificial intelligence image recognition

In order to find the defects of transmission lines accurately and timely, the three-dimensional inspection mode of transmission lines based on artificial intelligence image recognition technology is studied. Specifically, with the support of artificial intelligence image recognition technology, K-means algorithm is used to cluster the three dimensional inspection images. At the same time, artificial neural network is used for intelligent identification of transmission line defects in the image. According to the test, under the same workload, the transmission line defect identification without the use of artificial intelligence image recognition technology requires 5 image analysts to work continuously for 15 d, with an average of 2~3 images per minute and an image recognition speed of 20~30 s/piece; Using artificial intelligence recognition technology, recognition speed up to 0.25 s/sheet, only 3.6 h can complete the recognition task. In the low-light environment, the edge of the image is clearer after the enhanced force, and the target image is clearly separated from the background. At present, the recognition accuracy is higher than 90% by using 7 images from different angles of the same parts in the transmission line. In addition, by comparing the influence of constant learning strategies, AdaDec and AdaMix learning strategies on the convergence of the deep learning model under the same conditions, it is found that the reconstruction errors of the three strategies all show a tendency of gradually decreasing, and eventually tend to be stable. The results show that the artificial intelligence image recognition technology has a certain universality in the three-dimensional inspection image defect detection of transmission lines, and is conducive to the significant improvement of work efficiency.