With the development of both hardware and software technologies in camera and computer, automated visual inspection system is being used more and more in intelligent transportation system for its high efficiency. For the safety operation, it is necessary to perform fault inspection for train mechanical components. As one of the most widely used small mechanical components in freight trains, bogie block key (BBK) is used to keep wheel sets from separating out of bogies, and its fault is likely to cause terrible accidents. This study proposes a vision-based system to inspect the missing of BBK automatically. To ensure accurate and rapid fault inspection, a hierarchical detection framework consisting of fault area extraction and object detection is proposed. The purpose of fault area extraction is to divide image regions which contain the inspected component from the complex background. Subsequently, a component detector based on the sparse histograms of oriented gradients and support vector machine is proposed to verify the candidate image regions to check whether the BBK is missing or not. The experiments show that the proposed system realises the status inspection of BBK with high accuracy and high speed and can meet the need of actual applications.

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Vision-based fault inspection of small mechanical components for train safety

References

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