Multi-Scale Continuous Gradient Local Binary Pattern for Leaky Cable Fixture Detection in High-Speed Railway Tunnel

Abstract

The feature of leaky cable fixture extracted by Local Binary Pattern (LBP) and its variants in high-speed railway tunnel has the defects of lacking description and high dimension. This paper proposes a new operator named Multi-scale Continuous Gradient Local Binary Pattern (MCG-LBP), which can realize the scale transformation of feature maps and ensure the low dimensionality of descriptors. For MCG-LBP, firstly a bi-directional triplet around the central pixel is presented to indicate the specific direction of gradient in circle neighborhood. Then, an effective dimensionality reduction strategy is introduced to perform successive down-sampling iterations. Finally, the multi-scale joint descriptors are encoded by continuous gradient sequences from different down-sampling maps, and Support Vector Machines is used to classify faulty cable fixtures. The proposed MCG-LBP can elicit a discriminative description through complementary gradient information generated by the combination of different single-scale features. While the low dimensionality of descriptor and no complex parameter to deal with both make it has higher computational efficiency. Experimental results show that the Recall and Precision of MCG-LBP reach 92.6% and 83.5% respectively on cable fixture data set, which is superior to the state-of-the-art methods.

Keywords

• MCG-LBP
• bi-directional triplet
• dimensionality reduction
• fault detection
• leaky cable fixture
• support vector machines