IEICE TRANSACTIONS on Information
Loosening Bolts Detection of Bogie Box in Metro Vehicles Based on Deep Learning
Summary :
Bolts in the bogie box of metro vehicles are fasteners which are significant for bogie box structure. Effective loosening bolts detection in early stage can avoid the bolt loss and accident occurrence. Recently, detection methods based on machine vision are developed for bolt loosening. But traditional image processing and machine learning methods have high missed rate and false rate for bolts detection due to the small size and complex background. To address this problem, a loosening bolts defection method based on deep learning is proposed. The proposed method cascades two stages in a coarse-to-fine manner, including location stage based on the Single Shot Multibox Detector (SSD) and the improved SSD sequentially localizing the bogie box and bolts and a semantic segmentation stage with the U-shaped Network (U-Net) to detect the looseness of the bolts. The accuracy and effectiveness of the proposed method are verified with images captured from the Shanghai Metro Line 9. The results show that the proposed method has a higher accuracy in detecting the bolts loosening, which can guarantee the stable operation of the metro vehicles.
- Publication
- IEICE TRANSACTIONS on Information Vol.E105-D No.11 pp.1990-1993
- Publication Date
- 2022/11/01
- Publicized
- 2022/07/28
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2022EDL8041
- Type of Manuscript
- LETTER
- Category
- Image Recognition, Computer Vision
Authors
Weiwei QI
Shanghai University of Engineering Science
Shubin ZHENG
Shanghai University of Engineering Science
Liming LI
Shanghai University of Engineering Science
Zhenglong YANG
Shanghai University of Engineering Science
Keyword
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Weiwei QI, Shubin ZHENG, Liming LI, Zhenglong YANG, "Loosening Bolts Detection of Bogie Box in Metro Vehicles Based on Deep Learning" in IEICE TRANSACTIONS on Information,
vol. E105-D, no. 11, pp. 1990-1993, November 2022, doi: 10.1587/transinf.2022EDL8041.
Abstract: Bolts in the bogie box of metro vehicles are fasteners which are significant for bogie box structure. Effective loosening bolts detection in early stage can avoid the bolt loss and accident occurrence. Recently, detection methods based on machine vision are developed for bolt loosening. But traditional image processing and machine learning methods have high missed rate and false rate for bolts detection due to the small size and complex background. To address this problem, a loosening bolts defection method based on deep learning is proposed. The proposed method cascades two stages in a coarse-to-fine manner, including location stage based on the Single Shot Multibox Detector (SSD) and the improved SSD sequentially localizing the bogie box and bolts and a semantic segmentation stage with the U-shaped Network (U-Net) to detect the looseness of the bolts. The accuracy and effectiveness of the proposed method are verified with images captured from the Shanghai Metro Line 9. The results show that the proposed method has a higher accuracy in detecting the bolts loosening, which can guarantee the stable operation of the metro vehicles.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2022EDL8041/_p
Copy
@ARTICLE{e105-d_11_1990,
author={Weiwei QI, Shubin ZHENG, Liming LI, Zhenglong YANG, },
journal={IEICE TRANSACTIONS on Information},
title={Loosening Bolts Detection of Bogie Box in Metro Vehicles Based on Deep Learning},
year={2022},
volume={E105-D},
number={11},
pages={1990-1993},
abstract={Bolts in the bogie box of metro vehicles are fasteners which are significant for bogie box structure. Effective loosening bolts detection in early stage can avoid the bolt loss and accident occurrence. Recently, detection methods based on machine vision are developed for bolt loosening. But traditional image processing and machine learning methods have high missed rate and false rate for bolts detection due to the small size and complex background. To address this problem, a loosening bolts defection method based on deep learning is proposed. The proposed method cascades two stages in a coarse-to-fine manner, including location stage based on the Single Shot Multibox Detector (SSD) and the improved SSD sequentially localizing the bogie box and bolts and a semantic segmentation stage with the U-shaped Network (U-Net) to detect the looseness of the bolts. The accuracy and effectiveness of the proposed method are verified with images captured from the Shanghai Metro Line 9. The results show that the proposed method has a higher accuracy in detecting the bolts loosening, which can guarantee the stable operation of the metro vehicles.},
keywords={},
doi={10.1587/transinf.2022EDL8041},
ISSN={1745-1361},
month={November},}
Copy
TY - JOUR
TI - Loosening Bolts Detection of Bogie Box in Metro Vehicles Based on Deep Learning
T2 - IEICE TRANSACTIONS on Information
SP - 1990
EP - 1993
AU - Weiwei QI
AU - Shubin ZHENG
AU - Liming LI
AU - Zhenglong YANG
PY - 2022
DO - 10.1587/transinf.2022EDL8041
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E105-D
IS - 11
JA - IEICE TRANSACTIONS on Information
Y1 - November 2022
AB - Bolts in the bogie box of metro vehicles are fasteners which are significant for bogie box structure. Effective loosening bolts detection in early stage can avoid the bolt loss and accident occurrence. Recently, detection methods based on machine vision are developed for bolt loosening. But traditional image processing and machine learning methods have high missed rate and false rate for bolts detection due to the small size and complex background. To address this problem, a loosening bolts defection method based on deep learning is proposed. The proposed method cascades two stages in a coarse-to-fine manner, including location stage based on the Single Shot Multibox Detector (SSD) and the improved SSD sequentially localizing the bogie box and bolts and a semantic segmentation stage with the U-shaped Network (U-Net) to detect the looseness of the bolts. The accuracy and effectiveness of the proposed method are verified with images captured from the Shanghai Metro Line 9. The results show that the proposed method has a higher accuracy in detecting the bolts loosening, which can guarantee the stable operation of the metro vehicles.
ER -
English
