Detection of Power Line Insulator Defects Using Aerial Images Analyzed With Convolutional Neural Networks

By Xian Tao, Dapeng Zhang, Zihao Wang, Xilong Liu, Hongyan Zhang, De Xu. IEEE Transactions on Systems, Man & Cybernetics. Systems, 2020

640 citations recorded by the source corpus.

As the failure of power line insulators leads to the failure of power transmission systems, an insulator inspection system based on an aerial platform is widely used. Insulator defect detection is performed against complex backgrounds in aerial images, presenting an interesting but challenging problem. Traditional methods, based on handcrafted features or shallow-learning techniques, can only localize insulators and detect faults under specific detection conditions, such as when sufficient prior knowledge is available, with low background interference, at certain object scales, or under specific illumination conditions. This paper discusses the automatic detection of insulator defects using aerial images, accurately localizing insulator defects appearing in input images captured from real inspection environments. We propose a novel deep convolutional neural network (CNN) cascading architecture for performing localization and detecting defects in insulators. The cascading network uses a CNN based on a region proposal network to transform defect inspection into a two-level object detection problem. To address the scarcity of defect images in a real inspection environment, a data augmentation method is also proposed that includes four operations: 1) affine transformation; 2) insulator segmentation and background fusion; 3) Gaussian blur; and 4) brightness transformation. Defect detection precision and recall of the proposed method are 0.91 and 0.96 using a standard insulator dataset, and insulator defects under various conditions can be successfully detected. Experimental results demonstrate that this method meets the robustness and accuracy requirements for insulator defect detection.

Subfields: Civil and Structural Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/b0e5ec93c2648ec25a5afd51dc6ba9f171615abd

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In Computer Vision and Pattern Recognition

Citations in the corpus

A review on modern defect detection models using DCNNs – Deep convolutional neural networks (2021, background): “In [44] the authors proposed a system for detecting defects in power line insulators by using images acquired by a drone.”
A Review on State-of-the-Art Power Line Inspection Techniques (2020, background): “[136] proposed a cascading DCNN-based architecture for defect detection.”
Surface Defect Detection Methods for Industrial Products: A Review (2021, methodology): “Literature [45] proposed a cascaded structure based on Faster R-CNN, which converted the defect detection problem of power line insulators into a two-level target detection”