High-speed railway rod-insulator detection using segment clustering and deformable part models
Authors:
- 19 August 2016
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 3852-3856
Abstract
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.Keywords
This publication has 13 references indexed in Scilit:
- A Robust Insulator Detection Algorithm Based on Local Features and Spatial Orders for Aerial ImagesIEEE Geoscience and Remote Sensing Letters, 2014
- An Improved Unit-Linking PCNN for Segmentation of Infrared Insulator ImageApplied Mathematics & Information Sciences, 2014
- An Active Contour Model Based on Texture Distribution for Extracting Inhomogeneous Insulators From Aerial ImagesIEEE Transactions on Geoscience and Remote Sensing, 2013
- A Method of Insulator Detection from Video Sequence2012
- Recognition of insulator based on developed MPEG-7 texture feature2010
- Object Detection with Discriminatively Trained Part-Based ModelsIEEE Transactions on Pattern Analysis and Machine Intelligence, 2009
- A sparse texture representation using local affine regionsIEEE Transactions on Pattern Analysis and Machine Intelligence, 2005
- Histograms of Oriented Gradients for Human Detection2005
- A sparse texture representation using affine-invariant regions2003
- Topological structural analysis of digitized binary images by border followingComputer Vision, Graphics, and Image Processing, 1985