Abstract
The detection of insulators in power transmission and transformation inspection images is the basis for insulator state detection and fault diagnosis in thereafter. Aiming at the detection of insulators with different aspect ratios and scales and ones with mutual occlusion, a method of insulator inspection image based on the improved faster region-convolutional neural network (R-CNN) is put forward in this paper. By constructing a power transmission and transformation insulation equipment detection dataset and fine-tuning the faster R-CNN model, the anchor generation method and non-maximum suppression (NMS) in the region proposal network (RPN) of the faster R-CNN model were improved, thus realizing a better detection of insulators. The experimental results show that the average precision (AP) value of the faster R-CNN model was increased to 0.818 with the improved anchor generation method under the VGG-16 Net. In addition, the detection effect of different aspect ratios and different scales of insulators in the inspection images was improved significantly, and the occlusion of insulators could be effectively distinguished and detected using the improved NMS.
Highlights
As one of the most important infrastructures in power systems, insulators play an important role in the safe operation of transmission lines and substations [1]
A comparison of the detection results of the insulators in power transformation inspection images by the traditional region proposal network (RPN) and the improved RPN is demonstrated in transmission and transformation inspection images by the traditional RPN and the improved RPN
Are the experimental results obtained by the improved RPN
Summary
As one of the most important infrastructures in power systems, insulators play an important role in the safe operation of transmission lines and substations [1]. Insulators mostly work outdoors, which makes them prone to becoming dirty, cracked, and damaged, and threatens the safety and stability of power grids. For the insulator state detection and fault diagnosis thereafter, the accurate detection of insulators in power transmission and transformation inspection images provides a foundation and is of great importance. The continuous development of helicopters, unmanned aerial vehicles (UAVs), and other high-altitude operation platforms has brought new opportunities to power transmission and transformation inspection work [2]. The application of computer vision technologies for the detection of insulators in patrol inspection images has great significance for the running state of intelligent detection of insulators, and it can greatly save manpower and materials whilst improving monitoring efficiency [4] There are the following limitations in the process of manually judging patrol images: Firstly, it relies on inspection personnel with rich professional experience to avoid misjudgments or omissions; secondly, the flood of generated images or video data makes the maintenance speed too slow and the cost too high when only using manual judgment [3].
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