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Power transmission networks physically connect the power generators to theelectric consumers. Such systems extend over hundreds of kilometers. There aremany components in the transmission infrastructure that require a properinspection to guarantee flawless performance and reliable delivery, which, ifdone manually, can be very costly and time consuming. One essential componentis the insulator. Its failure can cause an interruption of the entiretransmission line or a widespread power failure. Automated fault detectioncould significantly decrease inspection time and related costs. Recently,several works have been proposed based on convolutional neural networks, whichaddress the issue mentioned above. However, existing studies focus on aspecific type of insulator faults. Thus, in this study, we introduce atwo-stage model that segments insulators from their background to then classifytheir states based on four different categories, namely: healthy, broken,burned/corroded and missing cap. The test results show that the proposedapproach can realize the effective segmentation of insulators and achieve highaccuracy in detecting several types of faults.

Segmentation and Defect Classification of the Power Line Insulators: A Deep Learning-based Approach