Affiliation:
1. Key Laboratory of Precision Opto-mechatronics Technology, Beihang University, People’s Republic of China
Abstract
Inspecting the condition of the key components of freight trains is an important task in the rail industry. Bolts on the wheel bearings are key components of a bogie, and bolt defects, such as missing or broken bolts, can lead to serious accidents. To improve the traditional manual inspection procedure, which is both laborious and inflexible, a novel method of automatic image recognition for bolt defects is proposed in this paper. The main procedures are as follows. When a freight train drives through the inspection station, images of the train’s wheels are captured by cameras installed alongside the track. Based on the local binary pattern descriptor, a support vector machine classifier is trained to distinguish between bolt and non-bolt images. The classifier is then combined with a rotate-and-slide window method to localize the three bolt regions in the wheel image. Specifically, a self-updating method is proposed in the training phase to automatically capture the various different situations experienced by bolts in real-life scenarios. After localization, we distinguish defective bolts from normal bolts based on whether there is a hexagonal shape in the bolt region. As demonstrated by real-life experiments, our proposed method can guarantee to find bolt defects and further work will be devoted to reducing the false alarm rate.
Cited by
12 articles.
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