Research on Object Detection and Combination Clustering for Railway Switch Machine Gap Detection

FENG Qingsheng; XIAO Shuai; LIU Wangyang; LI Hong

doi:10.23919/cje.2023.00.268

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Qingsheng FENG, Shuai XIAO, Wangyang LIU, et al., “Research on Object Detection and Combination Clustering for Railway Switch Machine Gap Detection,” Chinese Journal of Electronics, vol. x, no. x, pp. 1–18, xxxx doi: 10.23919/cje.2023.00.268

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Qingsheng FENG, Shuai XIAO, Wangyang LIU, et al., “Research on Object Detection and Combination Clustering for Railway Switch Machine Gap Detection,” Chinese Journal of Electronics, vol. x, no. x, pp. 1–18, xxxx doi: 10.23919/cje.2023.00.268

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Research on Object Detection and Combination Clustering for Railway Switch Machine Gap Detection

doi: 10.23919/cje.2023.00.268

FENG Qingsheng^{1
,
,},
XIAO Shuai^1
,,
LIU Wangyang^1
,,
LI Hong^2
,

1.
School of Automation and Electrical Engineering, Dalian Jiaotong University, Dalian 116028, China
2.
School of Software, Dalian Jiaotong University, Dalian 116028, China

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Author Bio:
Qingsheng FENG received his B.S. and M.S. degrees from Dalian Jiaotong University in 2001 and 2006, respectively, where he is now an Associate Professor. He has overseen and contributed to various projects funded by the Ministry of Education and Provincial Natural Science Foundation. His research interest areas include intelligent transportation information technology, as well as automation and control within rail transit. (Email: fqs@djtu.edu.cn)

Shuai XIAO was born in 2001, he is currently pursuing the master’s degree at Dalian Jiaotong University with a major in Traffic Information Engineering and Control. His current research interests are gap detection and intelligent fault diagnosis of railway switch machines. (Email: 17309558117@163.com)

Wangyang LIU graduated with a bachelor’s degree from Southwest Jiaotong University and is currently a master’s student at the School of Automation and Electrical Engineering, Dalian Jiaotong University. His current research direction is Automatic control and intelligent driving of train operation. (Email: liuwy20000711@163.com)

Hong LI received a master’s degree from Dalian Jiaotong University, Dalian, China, in 2006. She specializes in applied research within the realms of machine vision and deep learning. Her expertise encompasses network structure optimization and image processing. (Email: signal@djtu.edu.cn)
Corresponding author: Email: fqs@djtu.edu.cn
Received Date: 2022-03-22
Accepted Date: 2022-03-22

Available Online: 2024-03-01

Abstract

Abstract

Turnouts and switch machines play a crucial role in facilitating train line operations and establishing routes, making them vital for ensuring the safety and efficiency of railway transportation. Through the gap detection system of switch machines, the real-time working status of turnouts and switch machines on railway sites can be quickly known. However, due to the challenging working environment and demanding conversion tasks of switch machines, the current gap detection system has often experienced the issues of fault detection. To address this, this study proposes an automatic gap detection method for railway switch machines based on object detection and combination clustering. Firstly, a lightweight object detection network, specifically the MobileNetV3-YOLOv5s model, is used to accurately locate and extract the focal area. Subsequently, the extracted image undergoes preprocessing and is then fed into a combination clustering algorithm (SLIC-Canopy-KFCM) to achieve precise segmentation of the gap area and background. Finally, the Fisher optimal segmentation criterion is utilized to divide the data sequence of pixel values, determine the classification nodes and calculate the gap size. The experimental results obtained from switch machine gap images captured in various scenes demonstrate that the proposed method is capable of accurately locating focal areas, efficiently completing gap image segmentation with a segmentation accuracy of 93.55%, and swiftly calculating the gap size with a correct rate of 98.57%. Notably, the method achieves precise detection of gap sizes even after slight deflection of the acquisition camera, aligning it more closely with the actual conditions encountered on railway sites.
- Gap detection,
- Railway switch machine,
- Object detection,
- Combination clustering,
- Fisher optimal segmentation

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References(34)

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