The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Os parafusos na caixa do bogie dos veículos do metrô são fixadores importantes para a estrutura da caixa do bogie. A detecção eficaz de parafusos afrouxados no estágio inicial pode evitar a perda do parafuso e a ocorrência de acidentes. Recentemente, métodos de detecção baseados em visão artificial foram desenvolvidos para afrouxamento de parafusos. Mas os métodos tradicionais de processamento de imagem e aprendizado de máquina apresentam alta taxa de falhas e taxas falsas para detecção de parafusos devido ao tamanho pequeno e ao fundo complexo. Para resolver este problema, é proposto um método de deserção de parafusos afrouxados baseado em aprendizado profundo. O método proposto cascata dois estágios de maneira grosseira a fina, incluindo o estágio de localização baseado no Single Shot Multibox Detector (SSD) e o SSD aprimorado, localizando sequencialmente a caixa do bogie e os parafusos e um estágio de segmentação semântica com a rede em forma de U (U-Net) para detectar a folga dos parafusos. A precisão e eficácia do método proposto são verificadas com imagens capturadas da Linha 9 do Metrô de Xangai. Os resultados mostram que o método proposto possui maior precisão na detecção do afrouxamento dos parafusos, o que pode garantir o funcionamento estável dos veículos do metrô.
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
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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
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@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},}
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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 -