Classification of Metro Facilities with Deep Neural Networks
Metro barrier-detection has been one of the most popular research fields. How to detect obstacles quickly and accurately during metro operation is the key issue in the study of automatic train operation. Intelligent monitoring systems based on computer vision not only complete safeguarding tasks eff...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2019-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2019/6782803 |
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| _version_ | 1850219037014884352 |
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| author | Deqiang He Zhou Jiang Jiyong Chen Jianren Liu Jian Miao Abid Shah |
| author_facet | Deqiang He Zhou Jiang Jiyong Chen Jianren Liu Jian Miao Abid Shah |
| author_sort | Deqiang He |
| collection | DOAJ |
| description | Metro barrier-detection has been one of the most popular research fields. How to detect obstacles quickly and accurately during metro operation is the key issue in the study of automatic train operation. Intelligent monitoring systems based on computer vision not only complete safeguarding tasks efficiently but also save a great deal of human labor. Deep convolutional neural networks (DCNNs) are the most state-of-the-art technology in computer vision tasks. In this paper, we evaluated the effectiveness in classifying the common facility images in metro tunnels based on Google’s Inception V3 DCNN. The model requires fewer computational resources. The number of parameters and the computational complexity are much smaller than similar DCNNs. We changed its architecture (the last softmax layer and the auxiliary classifier) and used transfer learning technology to retrain the common facility images in the metro tunnel. We use mean average precision (mAP) as the metric for performance evaluation. The results indicate that our recognition model achieved 90.81% mAP. Compared with the existing method, this method is a considerable improvement. |
| format | Article |
| id | doaj-art-7917ca3a2d914bfd9b6c174a45da7b3e |
| institution | OA Journals |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-7917ca3a2d914bfd9b6c174a45da7b3e2025-08-20T02:07:31ZengWileyJournal of Advanced Transportation0197-67292042-31952019-01-01201910.1155/2019/67828036782803Classification of Metro Facilities with Deep Neural NetworksDeqiang He0Zhou Jiang1Jiyong Chen2Jianren Liu3Jian Miao4Abid Shah5Guangxi Key Laboratory of Manufacturing System & Advanced Manufacturing Technology, College of Mechanical Engineering, Guangxi University, 530004, Nanning, ChinaGuangxi Key Laboratory of Manufacturing System & Advanced Manufacturing Technology, College of Mechanical Engineering, Guangxi University, 530004, Nanning, ChinaGuangxi Key Laboratory of Manufacturing System & Advanced Manufacturing Technology, College of Mechanical Engineering, Guangxi University, 530004, Nanning, ChinaGuangxi Key Laboratory of Manufacturing System & Advanced Manufacturing Technology, College of Mechanical Engineering, Guangxi University, 530004, Nanning, ChinaGuangxi Key Laboratory of Manufacturing System & Advanced Manufacturing Technology, College of Mechanical Engineering, Guangxi University, 530004, Nanning, ChinaGuangxi Key Laboratory of Manufacturing System & Advanced Manufacturing Technology, College of Mechanical Engineering, Guangxi University, 530004, Nanning, ChinaMetro barrier-detection has been one of the most popular research fields. How to detect obstacles quickly and accurately during metro operation is the key issue in the study of automatic train operation. Intelligent monitoring systems based on computer vision not only complete safeguarding tasks efficiently but also save a great deal of human labor. Deep convolutional neural networks (DCNNs) are the most state-of-the-art technology in computer vision tasks. In this paper, we evaluated the effectiveness in classifying the common facility images in metro tunnels based on Google’s Inception V3 DCNN. The model requires fewer computational resources. The number of parameters and the computational complexity are much smaller than similar DCNNs. We changed its architecture (the last softmax layer and the auxiliary classifier) and used transfer learning technology to retrain the common facility images in the metro tunnel. We use mean average precision (mAP) as the metric for performance evaluation. The results indicate that our recognition model achieved 90.81% mAP. Compared with the existing method, this method is a considerable improvement.http://dx.doi.org/10.1155/2019/6782803 |
| spellingShingle | Deqiang He Zhou Jiang Jiyong Chen Jianren Liu Jian Miao Abid Shah Classification of Metro Facilities with Deep Neural Networks Journal of Advanced Transportation |
| title | Classification of Metro Facilities with Deep Neural Networks |
| title_full | Classification of Metro Facilities with Deep Neural Networks |
| title_fullStr | Classification of Metro Facilities with Deep Neural Networks |
| title_full_unstemmed | Classification of Metro Facilities with Deep Neural Networks |
| title_short | Classification of Metro Facilities with Deep Neural Networks |
| title_sort | classification of metro facilities with deep neural networks |
| url | http://dx.doi.org/10.1155/2019/6782803 |
| work_keys_str_mv | AT deqianghe classificationofmetrofacilitieswithdeepneuralnetworks AT zhoujiang classificationofmetrofacilitieswithdeepneuralnetworks AT jiyongchen classificationofmetrofacilitieswithdeepneuralnetworks AT jianrenliu classificationofmetrofacilitieswithdeepneuralnetworks AT jianmiao classificationofmetrofacilitieswithdeepneuralnetworks AT abidshah classificationofmetrofacilitieswithdeepneuralnetworks |