Condition monitoring ensures the safety of freight railroad operations. With the development of machine vision technology, visual inspection has become a principal means of condition monitoring. The brake shoe key （...Condition monitoring ensures the safety of freight railroad operations. With the development of machine vision technology, visual inspection has become a principal means of condition monitoring. The brake shoe key （BSK） is an important component in the brake system, and its absence will lead to serious accidents. This paper presents a novel method for automated visual inspection of the BSK condition in freight cars. BSK images are first acquired by hardware devices. The subsequent in- spection process is divided into three stages： first, the region-of-interest （ROI） is segmented from the source image by an im- proved spatial pyramid matching scheme based on multi-scale census transform （MSCT）. To localize the BSK in the ROI, cen- sus transform （CT） on gradient images is developed in the second stage. Then gradient encoding histogram （GEH） features and linear support vector machines （SVMs） are used to generate a BSK localization classifier. In the last stage, a condition classifier is trained by SVM, but the features are extracted from gray images. Finally, the ROI, BSK localization, and condition classifiers are cascaded to realize a completely automated inspection system. Experimental results show that the system achieves a correct inspection rate of 99.2% and a speed of 5 frames/s, which represents a good real-time performance and high recognition accuracy.展开更多
基金supported by the Special-Funded Programme on National Key Scientific Instruments and Equipment Development(No.2012YQ140032)the National Natural Science Foundation of China(No.51179076)+2 种基金the Jiangsu Province Postdoctoral Research Funding Plan(No.1402012B)the Scientific Research Foundation of Jiangsu University for Senior Personnel(No.14JDG134)the Jiangsu Province Science and Technology Support Plan(Industrial)(No.BE2012149)
文摘Condition monitoring ensures the safety of freight railroad operations. With the development of machine vision technology, visual inspection has become a principal means of condition monitoring. The brake shoe key （BSK） is an important component in the brake system, and its absence will lead to serious accidents. This paper presents a novel method for automated visual inspection of the BSK condition in freight cars. BSK images are first acquired by hardware devices. The subsequent in- spection process is divided into three stages： first, the region-of-interest （ROI） is segmented from the source image by an im- proved spatial pyramid matching scheme based on multi-scale census transform （MSCT）. To localize the BSK in the ROI, cen- sus transform （CT） on gradient images is developed in the second stage. Then gradient encoding histogram （GEH） features and linear support vector machines （SVMs） are used to generate a BSK localization classifier. In the last stage, a condition classifier is trained by SVM, but the features are extracted from gray images. Finally, the ROI, BSK localization, and condition classifiers are cascaded to realize a completely automated inspection system. Experimental results show that the system achieves a correct inspection rate of 99.2% and a speed of 5 frames/s, which represents a good real-time performance and high recognition accuracy.
