IN-YOLO: Real-Time Detection of Outdoor High Voltage Insulators Using UAV Imaging

作者：Diana Sadykova, Damira Pernebayeva, Mehdi Bagheri, Alex Pappachen James · 发表于：IEEE Transactions on Power Delivery · 年份：2019 · DOI：10.1109/tpwrd.2019.2944741 · 被引用次数：308 · 研究领域：Advanced Neural Network Applications、Image Enhancement Techniques、Power Line Inspection Robots

The high voltage insulator requires continuous monitoring and inspection to prevent failures and emergencies. Manual inspections are costly as it requires covering a large geographical area where insulators are often subjected to harsh weather conditions. Automatic detection of insulators from aerial images is the first step towards performing real-time classification of insulator conditions using Unmanned Aerial Vehicle (UAV). In this paper, we provide a cost-effective solution for detecting insulators under the conditions of an uncluttered background, varied object resolution and illumination conditions using You Only Look Once (YOLO) deep learning neural network model from aerial images. We apply data augmentation to avoid overfitting with a training set size of 56000 image samples. It is demonstrated experimentally that this method can accurately locate insulator on UAV based real-time image data. The detected insulator images are then successfully subjected to insulator surface condition assessment for the presence of ice, snow and water using different classifiers.