A Hierarchical Features-Based Model for Freight Train Defect Inspection

• Published in: IEEE sensors journal Vol. 20; no. 5; pp. 2671 - 2678
• Main Authors: Xiao, Ling, Wu, Bo, Hu, Youmin, Liu, Jie
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.03.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Convolution; Convolution neural network; Data mining; defect detection; Defects; Fasteners; Feature extraction; Freight traffic; freight train; Freight trains; hierarchical features; Image detection; Image processing; Image quality; Inspection; instance prediction; Machine learning; Multiscale analysis; Neural networks; Performance degradation; Proposals; Sensors; Traffic safety
• ISSN: 1530-437X; 1558-1748
• DOI: 10.1109/JSEN.2019.2954124

The diagnosis of freight train defects is essential for railway traffic safety. Recently, many freight train defect detection systems based on image processing were developed. However, most of them used hand-engineered features, whose performance was easily degraded by the low image quality and dynamic background. The deep learning method has the merit of extracting robust multi-scale information from images. In this paper, a hierarchical features-based instance detection (HID) model was developed for instance-level defect detections. A deep residual neural network model was firstly used to extract invariant and discriminative features. Then, acquired multi-scale features were processed by a region proposal neural network to generate a coarse defect region and make defect classification. Finally, instance-level predictions were made on the coarse defect region using acquired features. Experiments on the collected dataset that has six types of defects demonstrated the effectiveness of the HID. Additionally, comparisons with the state-of-the-art approaches further demonstrated its promising performance. We also verified its high performance in transfer learning.