论文标题
电源线绝缘子的分割和缺陷分类：一种基于深度学习的方法
Segmentation and Defect Classification of the Power Line Insulators: A Deep Learning-based Approach
论文作者
论文摘要
电力传输网络将发电机物理连接到电动消费者。这样的系统延伸了数百公里。传输基础设施中有许多组件需要进行适当的检查以确保完美的性能和可靠的交付，如果手动完成，这可能会非常昂贵且耗时。一个必不可少的组件是绝缘体。它的故障会导致整个传输线的中断或广泛的功率故障。自动故障检测可能会大大减少检查时间和相关成本。最近，基于卷积神经网络提出了几项作品，这些作品解决了上述问题。但是，现有的研究集中于特定类型的绝缘体断层。因此，在这项研究中，我们介绍了一个两阶段模型，该模型将绝缘子从其背景中分离出来，然后根据四个不同类别进行分类，即：健康，破碎，烧伤/腐蚀和缺失的上限。测试结果表明，所提出的方法可以实现绝缘子的有效分割，并在检测几种类型的故障方面具有很高的精度。
Power transmission networks physically connect the power generators to the electric consumers. Such systems extend over hundreds of kilometers. There are many components in the transmission infrastructure that require a proper inspection to guarantee flawless performance and reliable delivery, which, if done manually, can be very costly and time consuming. One essential component is the insulator. Its failure can cause an interruption of the entire transmission line or a widespread power failure. Automated fault detection could significantly decrease inspection time and related costs. Recently, several works have been proposed based on convolutional neural networks, which address the issue mentioned above. However, existing studies focus on a specific type of insulator faults. Thus, in this study, we introduce a two-stage model that segments insulators from their background to then classify their states based on four different categories, namely: healthy, broken, burned/corroded and missing cap. The test results show that the proposed approach can realize the effective segmentation of insulators and achieve high accuracy in detecting several types of faults.
