Journal
COMPUTERS & ELECTRICAL ENGINEERING
Volume 102, Issue -, Pages -Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.compeleceng.2022.108269
Keywords
Surface defect detection; Mask R-CNN; Deep learning; Multi-scale feature fusion; Region proposal net
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This paper proposes a new surface defect detection network based on Mask R-CNN to detect rail defects, achieving high accuracy in defect location through multi-scale fusion, a new evaluation metric, and data augmentation.
Rail surface defects are serious to the quality and safety of railroad system operation. Due to the diversity and randomness of rail defects form, the detection of rail surface defects is a challenging task. Therefore, this paper proposes a new surface defect detection network based on Mask R-CNN to detect rail defects. The detection network is designed with a new feature pyramid for multi -scale fusion; a new evaluation metric complete intersection over union (CIOU) is used in the region proposal network to overcome the limitations of intersection over union (IOU) in some special cases; in the training phase, both transfer learning and data augmentation are used to solve the problem of small defective datasets. The experimental evaluation shows that the model proposed in this paper achieves 98.70% mean average precision (MAP) on the proposed dataset and can locate the defect location more accurately.
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