Rail surface defect detection based on improved Mask R-CNN.

• Dual fusion feature pyramids offer better performance. • The candidate box obtained by using CIOU is more friendly. • Transfer learning and data augmentation can improve the problem of data scarcity. 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. [Display omitted] [ABSTRACT FROM AUTHOR]