Abstract
The condition monitoring of railway track line is one of the essential tasks to ensure the safety of the railway transportation system. Railway track line is mainly composed of tracks, fasteners, sleepers, and so on. Given the requirements for rapid and accurate inspection, innovative and intelligent methods for multi-target defect identification of the railway track line using image processing and deep learning methods are proposed in this paper. Firstly, the track and fastener positioning method based on variance projection and wavelet transform is introduced. After that, a bag-of-visual-word (BOVW) model combined with spatial pyramid decomposition is proposed for railway track line multi-target defect detection with a detection accuracy of 96.26%. Secondly, an improved YOLOv3 model named TLMDDNet (Track Line Multi-target Defect Detection Network), integrating scale reduction and feature concatenation, is proposed to enhance detection accuracy and efficiency. Finally, to reduce model complexity and further improve the detection speed, with the help of dense connection structure, a lightweight design strategy for the TLMDDNet model named DC-TLMDDNet (Dense Connection Based TLMDDNet) is proposed, in which the DenseNet is applied to optimize feature extraction layers in the backbone network of TLMDDNet. The effectiveness of the proposed methods is demonstrated by the experimental results.
Highlights
In recent years, the rapid development of rail transit puts more stringent demands on transportation safety and maintenance decisions
Based on the positioning results, a BOVW model combined with spatial pyramid decomposition is applied to the classification
To simplify the detection procedure and improve the detection accuracy, the TLMDDNet based on YOLOv3 is proposed for the considered issues
Summary
The rapid development of rail transit puts more stringent demands on transportation safety and maintenance decisions. The main contribution of this paper includes the following four aspects: 1) Based on advanced image processing technologies and deep learning networks, the problem of multi-target defect identification for the railway track line is studied and solved for the first time, and the proposed methods meet the demands for the inspection task of the railway track line. To improve detection accuracy and efficiency, and be more suitable for railway track line multitarget defect identification, an improved YOLOv3 model with scale reduction and feature concatenation is proposed in this paper, referred to as TLMDDNet. to reduce the parameter number of TLMDDNet and maintain a sound detection performance, the dense block in DenseNet is considered to replace part of the residual blocks in the TLMDDNet backbone network, referred to as DC-TLMDDNet. The proposed DC-TLMDDNet based method can reduce the complexity of TLMDDNet and further improve the detection speed. More details about Dense-SIFT can be found in [37]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
