25 May 2018 Railway equipment detection using exact height function shape descriptor based on fast adaptive Markov random field
Author Affiliations +
Abstract
This paper proposes a hierarchical feature-matching model for the typical faults detection, which is a big challenge in the trouble of a moving freight car detection system (TFDS) due to the constant color and complex background of images. The proposed model divides fault detection into two stages: image segmentation and parallel shape matching. In the process of segmentation, a fast adaptive Markov random field (FAMRF) algorithm is presented based on the image pyramid model and affinity propagation theory. In the process of shape matching, a shape descriptor named exact height function (EHF) is introduced on the basis of parallel dynamic programming. The experimental results indicate that the proposed hierarchical model combined with FAMRF and EHF can achieve automatic detection of an air brake system, bogie block key, and fastening bolt. The proposed model achieves high detection accuracy and great robustness, and it can be effectively applied to the fault detection in TFDS.
© 2018 Society of Photo-Optical Instrumentation Engineers (SPIE)
Guodong Sun, Guodong Sun, Yang Zhang, Yang Zhang, Hanbing Tang, Hanbing Tang, Huiming Zhang, Huiming Zhang, Moyun Liu, Moyun Liu, Daxing Zhao, Daxing Zhao, } "Railway equipment detection using exact height function shape descriptor based on fast adaptive Markov random field," Optical Engineering 57(5), 053114 (25 May 2018). https://doi.org/10.1117/1.OE.57.5.053114 . Submission: Received: 8 March 2018; Accepted: 10 May 2018
Received: 8 March 2018; Accepted: 10 May 2018; Published: 25 May 2018
JOURNAL ARTICLE
14 PAGES


SHARE
RELATED CONTENT

Image analysis for object detection in millimetre-wave images
Proceedings of SPIE (December 07 2004)
Laser image segmentation on edge detection
Proceedings of SPIE (October 26 2006)
Extraction of tire size code using local phase
Proceedings of SPIE (November 23 2009)

Back to Top