Automated Visual Inspection System for Bogie Block Key Under Complex Freight Train Environment

• Published in: IEEE transactions on instrumentation and measurement Vol. 65; no. 1; pp. 2 - 14
• Main Authors: Liu, Liu, Zhou, Fuqiang, He, Yuzhu
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2016; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: AdaBoost; automated visual inspection system; Blocking; Cameras; cascaded detector; Detectors; Faults; Feature extraction; Freight trains; gradient coded co-occurrence matrix (GCCM) features; hierarchical inspection framework; Image acquisition; Inspection; Inspections; support vector machine (SVM); Support vector machines; Training; Undercarriage; Visual inspection; Visualization; AdaBoost; gradient coded co-occurrence matrix (GCCM) features; hierarchical inspection framework; support vector machine (SVM); automated visual inspection system; cascaded detector
• ISSN: 0018-9456; 1557-9662
• DOI: 10.1109/TIM.2015.2479101

With the development of hardware and software in camera and computing units, visual inspection system (VIS) plays an increasing significant role in fault inspection task. This paper proposes a VIS to inspect the missing of bogie block key (BBK) used on freight trains. BBK is an important component to keep wheel sets from separating out of bogies. The missing of BBK is one of the most common faults threatening the running safety. VIS first acquires a bogie image by the image acquisition system, and then a hierarchical inspection framework containing bearing cap (BC) detection, fault region localization, and BBK classification is proposed. Specifically, a cascaded detector trained by the AdaBoost approach combined with the gradient coded co-occurrence matrix (GCCM) features is used to achieve fast and accurate BC detection. Then, a fault region is located on the basis of the relationship between the BC and BBK. Finally, a BBK classifier based on the GCCM features and a support vector machine is used to process the fault region to identify the missing of BBK. The proposed system has been applied on a long sequence of real images showing high inspection speed and accuracy.