A Method of Trackside Kilometer Post Identification Combined with YOLOv3 Model
In view of poor anti-interference ability and weak generalization of the present trackside identification method, it is unable to adapt to complex background fluctuation along railway. Therefore, an image recognition method based on YOLOv3 was proposed, which could still ensure good recognition accu...
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| Format: | Article |
| Language: | zho |
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Editorial Office of Control and Information Technology
2020-01-01
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| Series: | Kongzhi Yu Xinxi Jishu |
| Subjects: | |
| Online Access: | http://ctet.csrzic.com/thesisDetails#10.13889/j.issn.2096-5427.2020.05.002 |
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| _version_ | 1849224948509310976 |
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| author | QIU Xinhua WANG Wenkun JI Yuwen LI Jia |
| author_facet | QIU Xinhua WANG Wenkun JI Yuwen LI Jia |
| author_sort | QIU Xinhua |
| collection | DOAJ |
| description | In view of poor anti-interference ability and weak generalization of the present trackside identification method, it is unable to adapt to complex background fluctuation along railway. Therefore, an image recognition method based on YOLOv3 was proposed, which could still ensure good recognition accuracy in the face of different illumination, complex background and different forms of image. Firstly, a convolutional neural network is used to learn image features and obtain network parameters of each layer. Transfer learning is adopted to obtain possible rectangular region of kilometer post based on the trained network parameters. Then, pattern recognition is used to extract, segment and recognize character region of kilometer scale, and finally output digital information of kilometer scale to the control system. Experimental results show that the identification time is about 0.04 s and the positioning accuracy is within 0.5 m by this method, which meets the accuracy and real-time requirements of position correction for rail flaw detection vehicle system. |
| format | Article |
| id | doaj-art-55b04b488ce549e2852a76ffef3343ef |
| institution | Kabale University |
| issn | 2096-5427 |
| language | zho |
| publishDate | 2020-01-01 |
| publisher | Editorial Office of Control and Information Technology |
| record_format | Article |
| series | Kongzhi Yu Xinxi Jishu |
| spelling | doaj-art-55b04b488ce549e2852a76ffef3343ef2025-08-25T06:49:46ZzhoEditorial Office of Control and Information TechnologyKongzhi Yu Xinxi Jishu2096-54272020-01-013771182323052A Method of Trackside Kilometer Post Identification Combined with YOLOv3 ModelQIU XinhuaWANG WenkunJI YuwenLI JiaIn view of poor anti-interference ability and weak generalization of the present trackside identification method, it is unable to adapt to complex background fluctuation along railway. Therefore, an image recognition method based on YOLOv3 was proposed, which could still ensure good recognition accuracy in the face of different illumination, complex background and different forms of image. Firstly, a convolutional neural network is used to learn image features and obtain network parameters of each layer. Transfer learning is adopted to obtain possible rectangular region of kilometer post based on the trained network parameters. Then, pattern recognition is used to extract, segment and recognize character region of kilometer scale, and finally output digital information of kilometer scale to the control system. Experimental results show that the identification time is about 0.04 s and the positioning accuracy is within 0.5 m by this method, which meets the accuracy and real-time requirements of position correction for rail flaw detection vehicle system.http://ctet.csrzic.com/thesisDetails#10.13889/j.issn.2096-5427.2020.05.002deep learningobject detection network modelposition correctionkilometer postrail flaw detection car |
| spellingShingle | QIU Xinhua WANG Wenkun JI Yuwen LI Jia A Method of Trackside Kilometer Post Identification Combined with YOLOv3 Model Kongzhi Yu Xinxi Jishu deep learning object detection network model position correction kilometer post rail flaw detection car |
| title | A Method of Trackside Kilometer Post Identification Combined with YOLOv3 Model |
| title_full | A Method of Trackside Kilometer Post Identification Combined with YOLOv3 Model |
| title_fullStr | A Method of Trackside Kilometer Post Identification Combined with YOLOv3 Model |
| title_full_unstemmed | A Method of Trackside Kilometer Post Identification Combined with YOLOv3 Model |
| title_short | A Method of Trackside Kilometer Post Identification Combined with YOLOv3 Model |
| title_sort | method of trackside kilometer post identification combined with yolov3 model |
| topic | deep learning object detection network model position correction kilometer post rail flaw detection car |
| url | http://ctet.csrzic.com/thesisDetails#10.13889/j.issn.2096-5427.2020.05.002 |
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