High-speed railway rod-insulator detection using segment clustering and deformable part models

Catenary system maintenance is an important task to the operation of a high-seed railway system. Currently, the inspection of damaged parts in the catenary system is performed manually, which is often slow and unreliable. This paper proposes a method to detect and locate the rod-insulators in the image taken from the high-speed railway catenary system. Sub-images containing bar-shaped devices such as cantilever, strut, rod, and pole are first extracted from the image. Rod-insulator is then recognized and detected from these bar-shaped sub-images by using deformable part models and latent SVM. Experimental results show that the proposed method is able to locate rod-insulators accurately from the catenary image for the subsequent detect inspection process. The robustness of this method ensures its performance in different imaging conditions.