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Robust Powerline Equipment Inspection System Based on a Convolutional Neural Network

机译：基于卷积神经网络的鲁棒电力线设备检查系统

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摘要

Electric power line equipment such as insulators, cut-out-switches, and lightning-arresters play important roles in ensuring a safe and uninterrupted power supply. Unfortunately, their continuous exposure to rugged environmental conditions may cause physical or electrical defects in them which may lead to the failure to the electrical system. In this paper, we present an automatic real-time electrical equipment detection and defect analysis system. Unlike previous handcrafted feature-based approaches, the proposed system utilizes a Convolutional Neural Network (CNN)-based equipment detection framework, making it possible to detect 17 different types of powerline insulators in a highly cluttered environment. We also propose a novel rotation normalization and ellipse detection method that play vital roles in the defect analysis process. Finally, we present a novel defect analyzer that is capable of detecting gunshot defects occurring in electrical equipment. The proposed system uses two cameras; a low-resolution camera that detects insulators from long-shot images, and a high-resolution camera which captures close-shot images of the equipment at high-resolution that helps for effective defect analysis. We demonstrate the performances of the proposed real-time equipment detection with up to 93% recall with 92% precision, and defect analysis system with up to 98% accuracy, on a large evaluation dataset. Experimental results show that the proposed system achieves state-of-the-art performance in automatic powerline equipment inspection.
机译：诸如绝缘子，断路器，避雷器之类的电力线设备在确保安全和不间断电源方面发挥着重要作用。不幸的是，它们持续暴露于恶劣的环境条件下可能会导致其中的物理或电气缺陷，从而导致电气系统故障。在本文中，我们提出了一种自动的实时电气设备检测和缺陷分析系统。与以前的手工基于特征的方法不同，所提出的系统利用基于卷积神经网络（CNN）的设备检测框架，从而可以在高度混乱的环境中检测17种不同类型的电力线绝缘子。我们还提出了一种新颖的旋转归一化和椭圆检测方法，该方法在缺陷分析过程中起着至关重要的作用。最后，我们提出了一种新颖的缺陷分析仪，它能够检测电气设备中发生的枪击缺陷。提议的系统使用两个摄像头。低分辨率相机可从长时间拍摄的图像中检测绝缘体，而高分辨率相机则可以以高分辨率捕获设备的近距离图像，从而有助于有效地分析缺陷。我们在大型评估数据集上演示了建议的实时设备检测的性能，召回率高达93％，准确度为92％，缺陷分析系统的准确度高达98％。实验结果表明，该系统在电力线设备自动检查中达到了最先进的性能。

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