Research on online inspection of pantograph and catenary based on deep learning
The pantograph-catenary system is a key part of power supply for electric locomotive, and its operating status determines the current receiving quality of electric locomotive, as well as safety and efficiency of trains. In order to solve the problems of traditional method for inspection of pantograp...
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| Format: | Article |
| Language: | zho |
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Editorial Department of Electric Drive for Locomotives
2022-09-01
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| Series: | 机车电传动 |
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| Online Access: | http://edl.csrzic.com/thesisDetails#10.13890/j.issn.1000-128X.2022.05.020 |
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| _version_ | 1850186582802300928 |
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| author | ZHOU Zhaoan LI Shuzhi |
| author_facet | ZHOU Zhaoan LI Shuzhi |
| author_sort | ZHOU Zhaoan |
| collection | DOAJ |
| description | The pantograph-catenary system is a key part of power supply for electric locomotive, and its operating status determines the current receiving quality of electric locomotive, as well as safety and efficiency of trains. In order to solve the problems of traditional method for inspection of pantograph-catenary status, such as low efficiency and poor performance in real time inspection,this paper designed an online pantograph-catenary status inspection system solution based deep learning. The solution adopted NIDIA Xavier SoC module to perform image processing, and realizes pantograph-catenary inspection by YOLO v4 and adaptive image enhancement module was also added. The mAP of inspection target before and after optimization was 0.950 and 0.961 (with the IOU threshold of 0.5) respectively. Classification of catenary dropper status based on ViT lightweight class attention model was realized at an average accuracy rate of 97.69%. After acceleration by using NVIDIA TensorRT accelerator, the inference time of inspection model and classification model were 31.0 ms and 2.2 ms respectively. The system has high robustness and practicability, which can provide theory basis and design reference for online inspection function of pantograph-catenary abnormality in the future. |
| format | Article |
| id | doaj-art-58f0ed9a9ca342aab4d6eadd9a846ad3 |
| institution | OA Journals |
| issn | 1000-128X |
| language | zho |
| publishDate | 2022-09-01 |
| publisher | Editorial Department of Electric Drive for Locomotives |
| record_format | Article |
| series | 机车电传动 |
| spelling | doaj-art-58f0ed9a9ca342aab4d6eadd9a846ad32025-08-20T02:16:19ZzhoEditorial Department of Electric Drive for Locomotives机车电传动1000-128X2022-09-0113514332276573Research on online inspection of pantograph and catenary based on deep learningZHOU ZhaoanLI ShuzhiThe pantograph-catenary system is a key part of power supply for electric locomotive, and its operating status determines the current receiving quality of electric locomotive, as well as safety and efficiency of trains. In order to solve the problems of traditional method for inspection of pantograph-catenary status, such as low efficiency and poor performance in real time inspection,this paper designed an online pantograph-catenary status inspection system solution based deep learning. The solution adopted NIDIA Xavier SoC module to perform image processing, and realizes pantograph-catenary inspection by YOLO v4 and adaptive image enhancement module was also added. The mAP of inspection target before and after optimization was 0.950 and 0.961 (with the IOU threshold of 0.5) respectively. Classification of catenary dropper status based on ViT lightweight class attention model was realized at an average accuracy rate of 97.69%. After acceleration by using NVIDIA TensorRT accelerator, the inference time of inspection model and classification model were 31.0 ms and 2.2 ms respectively. The system has high robustness and practicability, which can provide theory basis and design reference for online inspection function of pantograph-catenary abnormality in the future.http://edl.csrzic.com/thesisDetails#10.13890/j.issn.1000-128X.2022.05.020pantograph-catenary status inspectioncatenarydropperdeep learningTensorRTViTYOLO v4 |
| spellingShingle | ZHOU Zhaoan LI Shuzhi Research on online inspection of pantograph and catenary based on deep learning 机车电传动 pantograph-catenary status inspection catenary dropper deep learning TensorRT ViT YOLO v4 |
| title | Research on online inspection of pantograph and catenary based on deep learning |
| title_full | Research on online inspection of pantograph and catenary based on deep learning |
| title_fullStr | Research on online inspection of pantograph and catenary based on deep learning |
| title_full_unstemmed | Research on online inspection of pantograph and catenary based on deep learning |
| title_short | Research on online inspection of pantograph and catenary based on deep learning |
| title_sort | research on online inspection of pantograph and catenary based on deep learning |
| topic | pantograph-catenary status inspection catenary dropper deep learning TensorRT ViT YOLO v4 |
| url | http://edl.csrzic.com/thesisDetails#10.13890/j.issn.1000-128X.2022.05.020 |
| work_keys_str_mv | AT zhouzhaoan researchononlineinspectionofpantographandcatenarybasedondeeplearning AT lishuzhi researchononlineinspectionofpantographandcatenarybasedondeeplearning |