A Method of Locating and Measuring Train Wheel Tread Defects Based on YOLOv3-tiny
Aiming at the problem of insufficient detection frame size accuracy in the deep learning based wheelset tread defect detection algorithm, this paper proposes a tread defect detection algorithm based on YOLOv3-tiny and traditional image algorithm, which can realize fast positioning of defects with lo...
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| Format: | Article |
| Language: | zho |
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Editorial Office of Control and Information Technology
2022-04-01
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| Series: | Kongzhi Yu Xinxi Jishu |
| Subjects: | |
| Online Access: | http://ctet.csrzic.com/thesisDetails#10.13889/j.issn.2096-5427.2022.02.200 |
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| _version_ | 1849224966129582080 |
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| author | JIN Kairong WANG Junping CHEN Shenglan |
| author_facet | JIN Kairong WANG Junping CHEN Shenglan |
| author_sort | JIN Kairong |
| collection | DOAJ |
| description | Aiming at the problem of insufficient detection frame size accuracy in the deep learning based wheelset tread defect detection algorithm, this paper proposes a tread defect detection algorithm based on YOLOv3-tiny and traditional image algorithm, which can realize fast positioning of defects with low CPU consumption and precise measurement of geometric parameters. First, image enhancement is performed on the small samples obtained in the industrial scene, and then the YOLOv3-tiny algorithm is used to perform migration learning on tread defects to achieve rough localization of defects. In order to solve the key problems that the detection frame is too large and too small, traditional image algorithms such as Fourier transform, band-stop filter, and threshold segmentation are used to construct a defect size measurement model, contours of roughly located defects are extracted and detection frame size is optimized, and finally location and size of defect are accurately calculated. Defect location experimental results show that the average accuracy of defect identification is 89.4% and the CPU consumption does not exceed 10% when IoU threshold is 0.5. Experimental results of defect measurement show that the algorithm can optimize 74 out of 90 inspection frames and obtain a more accurate defect size. The above experimental results show that the detection algorithm in this paper is effective in improving size accuracy of detection frame. |
| format | Article |
| id | doaj-art-94b4c61b77074aa3a19e7994e09d26aa |
| institution | Kabale University |
| issn | 2096-5427 |
| language | zho |
| publishDate | 2022-04-01 |
| publisher | Editorial Office of Control and Information Technology |
| record_format | Article |
| series | Kongzhi Yu Xinxi Jishu |
| spelling | doaj-art-94b4c61b77074aa3a19e7994e09d26aa2025-08-25T06:48:02ZzhoEditorial Office of Control and Information TechnologyKongzhi Yu Xinxi Jishu2096-54272022-04-01697524252872A Method of Locating and Measuring Train Wheel Tread Defects Based on YOLOv3-tinyJIN KairongWANG JunpingCHEN ShenglanAiming at the problem of insufficient detection frame size accuracy in the deep learning based wheelset tread defect detection algorithm, this paper proposes a tread defect detection algorithm based on YOLOv3-tiny and traditional image algorithm, which can realize fast positioning of defects with low CPU consumption and precise measurement of geometric parameters. First, image enhancement is performed on the small samples obtained in the industrial scene, and then the YOLOv3-tiny algorithm is used to perform migration learning on tread defects to achieve rough localization of defects. In order to solve the key problems that the detection frame is too large and too small, traditional image algorithms such as Fourier transform, band-stop filter, and threshold segmentation are used to construct a defect size measurement model, contours of roughly located defects are extracted and detection frame size is optimized, and finally location and size of defect are accurately calculated. Defect location experimental results show that the average accuracy of defect identification is 89.4% and the CPU consumption does not exceed 10% when IoU threshold is 0.5. Experimental results of defect measurement show that the algorithm can optimize 74 out of 90 inspection frames and obtain a more accurate defect size. The above experimental results show that the detection algorithm in this paper is effective in improving size accuracy of detection frame.http://ctet.csrzic.com/thesisDetails#10.13889/j.issn.2096-5427.2022.02.200tread defectobject detectionYOLOv3-tinyFourier transformdeep learning |
| spellingShingle | JIN Kairong WANG Junping CHEN Shenglan A Method of Locating and Measuring Train Wheel Tread Defects Based on YOLOv3-tiny Kongzhi Yu Xinxi Jishu tread defect object detection YOLOv3-tiny Fourier transform deep learning |
| title | A Method of Locating and Measuring Train Wheel Tread Defects Based on YOLOv3-tiny |
| title_full | A Method of Locating and Measuring Train Wheel Tread Defects Based on YOLOv3-tiny |
| title_fullStr | A Method of Locating and Measuring Train Wheel Tread Defects Based on YOLOv3-tiny |
| title_full_unstemmed | A Method of Locating and Measuring Train Wheel Tread Defects Based on YOLOv3-tiny |
| title_short | A Method of Locating and Measuring Train Wheel Tread Defects Based on YOLOv3-tiny |
| title_sort | method of locating and measuring train wheel tread defects based on yolov3 tiny |
| topic | tread defect object detection YOLOv3-tiny Fourier transform deep learning |
| url | http://ctet.csrzic.com/thesisDetails#10.13889/j.issn.2096-5427.2022.02.200 |
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