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首页> 外文期刊>Computers and Electrical Engineering >Insulator visual non-conformity detection in overhead power distribution lines using deep learning
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Insulator visual non-conformity detection in overhead power distribution lines using deep learning

机译：绝缘子使用深度学习的架空配电线路中的绝缘子视觉非符合性检测

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Overhead Power Distribution Lines (OPDLs) correspond to a large percentage of the medium-voltage electrical systems. In these networks, visual inspection activities are usually performed without resorting to automated systems, requiring a significant investment of time and human resources. We present a methodology to identify the defect and type of insulators using Convolutional Neural Networks (CNNs). More than 2500 photographs were collected both from inside a studio and from a realistic OPDL. A classification model is proposed to automatically recognize the insulators conformity. This model is able to learn from indoors photographs by augmenting these images with realistic details such as top ties and real-world backgrounds. Furthermore, Multi-Task Learning (MTL) was used to improve performance of defect detection by also predicting the insulator class. The proposed methodology is able to achieve an accuracy of 92% for material classification and 85% for defect detection, with F1-score of 0.75, surpassing available solutions. (C) 2019 Elsevier Ltd. All rights reserved.
机译：开销配电线（OPDL）对应于中压电气系统的大百分比。在这些网络中，通常在不诉诸自动化系统的情况下进行目视检查活动，需要大量的时间和人力资源投入。我们提出了一种方法来识别使用卷积神经网络（CNN）的缺陷和类型的绝缘子。从工作室内部和现实的OPDL都收集了超过2500张照片。提出分类模型以自动识别绝缘体符合性。该模型能够通过增强具有现实细节的这些图像来从室内照片学习，例如顶部领带和现实世界背景。此外，使用多任务学习（MTL）来通过预测绝缘类来改善缺陷检测的性能。所提出的方法能够实现材料分类的92％的精度，缺陷检测85％，F1分数为0.75，超越可用解决方案。 （c）2019年elestvier有限公司保留所有权利。

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