Missing Small Fastener Detection Using Deep Learning

Ling Xiao

doi:10.1109/TIM.2020.3023509

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Missing Small Fastener Detection Using Deep Learning

2021-01-01

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DOI：10.1109/TIM.2020.3023509

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Abstract

En 中文

Small fasteners are widely used in a freight train. However, due to the complex working environment, the missing of fasteners always happens, causing traffic accidents and property loss. Thus, detection of the missing of small fasteners is essential for the safety of freight trains. This article takes three critical missing small fasteners as examples, pin-missing, bolt-missing, and rivet-missing defects, and proposes a two-stage small defect detection model. First, a copy-pasting method is used to augment the defects in the training image, then the ResNet-101 backbone is adopted to extract defect features, and a feature pyramid network is used to construct a feature pyramid. After obtaining defect features, an improved region proposal network is introduced to generate defect proposals. Finally, a fully convolutional neural network is trained to operate pixelwise segmentation. The experimental results on manually collected fastener defect data set show that our model possesses high precision and efficiency on the inspection of pin-missing, bolt-missing, and rivet-missing defects. Besides, the proposed model could be used for similar small defects detection.

Keywords:

Data augmentation

fastener defects

parameter optimization

small defects

transfer learning

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