Railway Fastener Detection Algorithm Integrating PHOG and MSLBP Features

In order to improve the recognition rate and robustness of railway fasteners detection, and to increase the effectiveness of PHOG (pyramid histogram of oriented gradients) feature, a simple and effective sleeper shoulder locating algorithm was proposed. In this algorithm, the redundant information in fastener images was removed before extraction of PHOG feature, according to the positional relationships among the sleeper shoulder, fasteners, and background. Then, an MSLBP (macroscopic local binary pattern) sampling method was designed and applied to extract the macroscopic texture feature of the fastener images, which could well simulate human visual attention mechanism. Finally, the two different categories of features were integrated by the hierarchical weighted fusion method;using the SVM classifier to classify and detect fastener defects, a defect recognition algorithm based on computer vision and PHOG-MSLBP fusion feature was presented. The algorithm was applied to experiments, and the results show that the average recognition rate based on PHOG-MSLBP feature is 6.3% higher than that based on PHOG feature, and 4.5% higher than that based on MSLBP feature. In addition, the proposed algorithm is more robust than several mainstream methods, and can meet the need of automatic inspection of fastener defects.