A deep-learning-based approach for automated wagon component inspection

ABSTRACT

Inspecting objects in the industry aims to guarantee product quality allowing problems to be corrected and damaged products to be discarded. Inspection is also widely used in railway maintenance, where wagon components need to be checked due to efficiency and safety concerns. In some organizations, hundreds of wagons are inspected visually by a human inspector, which leads to quality issues and safety risks for the inspectors. This paper describes a wagon component inspection approach using Deep Learning techniques to detect a particular damaged component: the shear pad. We compared our approach for convolutional neural networks with the state of art classification methods to distinguish among three shear pads conditions: absent, damaged, and undamaged shear pad. Our results are very encouraging showing empirical evidence that our approach has better performance than other classification techniques.

References

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• Computing methodologies

  • Machine learning

    • Machine learning approaches