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.


  author = {M.~Oberweger and A.~Wendel and H.~Bischof},
  title = {Visual Recognition and Fault Detection for Power Line Insulators},
  booktitle = {Proc.~of Computer Vision Winter Workshop},
  year = 2014