Automatic visual inspection of a missing split pin in the China railway high-speed.

The split pin (SP) on the caliper brake is a vital component of the brake system of a bogie traveling along the China railway high-speed (CRH), and the absence of the SP could cause serious train accidents. A new automatic visual inspection method is proposed for the quick and accurate detection of SP faults of the CRH. The proposed approach is based on the histogram of gradient (HOG) combined with the complete local binary pattern (CLBP). First, a fast pyramid template matching technique is presented for localizing the region of interest to reduce the searching scope. Under the multiresolution pyramid model for target localization, a coarse-to-fine strategy is employed to ensure that the recognizing speed of the SP for the entire image is increased significantly. Second, a hierarchical framework is adopted at the localizing and inspecting stages of the SP to automatically implement the inspection tasks. To increase the robustness to the outside complex illumination, the HOG feature for localizing the target and the CLBP feature for examining the state of the SP (i.e., missing or not-missing) are extracted in the Sobel gradient domain. The localization and recognition stages are both fulfilled through the use of their respective intersection kernel support vector machine classifiers and corresponding features. In conclusion, experimental results indicate that the inspection system achieves a high accuracy rate of more than 99.0% and a real-time speed, thus proving that the proposed method is effective for the fault inspection of the SP and can satisfy the requirements of CRH's actual application.