A hybrid deep learning based framework for component defect detection of moving trains

• Main Authors: CHEN, Cen, LI, Kenli, CHENG, Zhongyao, PICCIALLI, Francesco, HOI, Steven C. H., ZENG, Zeng
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2022
• Subjects: Automatic defect detection; deep convolutional neural networks; Deep learning; Feature extraction; Image segmentation; Inspection; Object detection; Rail transportation; railway system; Semantics; train component defects; visual inspection; China; Databases and Information Systems; Transportation
• Online Access: https://ink.library.smu.edu.sg/sis_research/6181

Defect detection of trains is of great significance for operation safety and maintenance efficiency for railway maintenance. Nowadays, China railway system utilizes high-speed line scan cameras to capture images of critical parts of moving trains. The visual inspection on the images still heavily re...