The Integrated patrolling inspection train has been used worldwide for railway safety monitoring. The camera mounted under the train can capture the track image for abnormal fastener detection. For solving the high false positive alarm of rail fastener recognition arising from ballasts occlusion and non-uniform illumination, we proposed a fastener defect recognition method using deep learning model, and constructed four network structures based on AlexNet and ResNet to learn the fastener feature in complex background. The experimental results show that the RestNet18 network model with unfreezing convolutional layers not only performs well at the trained line, but also has good generalization at the new line, which is a more appropriate model for fastener recognition by comparison with the traditional handcraft feature and existing deep learning models.
Rail fastener automatic recognition method in complex background
Tenth International Conference on Digital Image Processing (ICDIP 2018) ; 2018 ; Shanghai,China
Proc. SPIE ; 10806
2018-08-09
Conference paper
Electronic Resource
English
Rail fastener automatic recognition method in complex background [10806-314]
British Library Conference Proceedings | 2018