Multi-Target Defect Identification for Railway Track Line Based on Image Processing and Improved YOLOv3 Model

IEEE Access

卷:8

Yujie Li¹  Dehua Wei²  Da Suo²  Xiukun Wei³  Limin Jia³ 

[1] Beijing Mass Transit Railway Operation Corporation Ltd., Beijing, China;

[2] School of Traffic and Transportation, Beijing Jiaotong University, Beijing, China;

[3] State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China;

关键词: Railway track line defects;    multi-target defect identification;    image processing;    deep learning;    YOLOv3;   

DOI  :  10.1109/ACCESS.2020.2984264

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown