  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.
