Visual Recognition and Fault Detection for Power Line Insulators

The inspection of high voltage power lines is an important task in order to prevent failure of the transmission system. In this work, we present a novel approach to detect insulators in aerial images and to analyze them automatically for possible faults. Our detection algorithm is based on discriminative training of local gradient-based descriptors and a subsequent voting scheme for localization. Further, we introduce an automatic extraction of the individual insulator caps and check them for faults by using a descriptor with elliptical spatial support. We demonstrate our approach on an evaluation set of 400 real-world insulator images captured from a helicopter and evaluate our results with respect to a manually created ground-truth. The performance of our insulator detector is comparable to other state-of-the-art object detectors and our insulator fault detection outperforms existing methods.