Identification and location method of strip ingot for autonomous robot system using kmeans clustering and color segmentation
Abstract In order to improve the efficiency of autonomous robot sorting steel ingots, this paper proposes a two‐dimensional weighted equivalent clustering‐based progressive probabilistic hough transform (2D‐WEC‐PPHT) algorithm for the problem of identifying and locating strip ingots in automatic pic...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-11-01
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| Series: | IET Control Theory & Applications |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/cth2.12481 |
| _version_ | 1827777132346998784 |
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| author | Qiguang Li Huazheng Zheng Tianwei Cui Yumeng Zhang |
| author_facet | Qiguang Li Huazheng Zheng Tianwei Cui Yumeng Zhang |
| author_sort | Qiguang Li |
| collection | DOAJ |
| description | Abstract In order to improve the efficiency of autonomous robot sorting steel ingots, this paper proposes a two‐dimensional weighted equivalent clustering‐based progressive probabilistic hough transform (2D‐WEC‐PPHT) algorithm for the problem of identifying and locating strip ingots in automatic picking strip ingot palletizing. First, the steel ingot image is preprocessed and the PPHT linear detection method is used to extract the edge linear information of the steel ingot. Second, the angle and position information are normalized and weighted, and a two‐dimensional clustering distance calculation method is proposed for two‐dimensional clustering of the extracted line information. Besides, the clustering line clusters with close angles were processed into equivalent lines by mean fitting. Then, the average threshold method was used to segment the ingot between each two fitting lines, and the minimum rectangle was used to box the segmentation part. The centroid of the box‐selected rectangle is the positioning center of the ingot, and the long side angle of the box‐selected rectangle is the deviation angle information of the ingot. Finally, the experimental results show that the number of redundant lines detected by the 2D‐WEC‐PPHT is significantly less than that of traditional methods such as HT and PPHT. The positioning speed of ingot is faster than that of HT and PPHT, and the processing time is reduced from 20 s to 10 s. In the case of large proportion of old ingots with weak reflection, the recognition accuracy reached 93.5%, and the angular and position positioning accuracy were 2.270° and 11.675 mm, respectively. The recognition accuracy of new ingots with strong reflection reached 99.574%, which met the requirements of picking positioning accuracy. |
| first_indexed | 2024-03-11T14:14:23Z |
| format | Article |
| id | doaj.art-94489e3319264bd0a2ca4537e02d332b |
| institution | Directory Open Access Journal |
| issn | 1751-8644 1751-8652 |
| language | English |
| last_indexed | 2024-03-11T14:14:23Z |
| publishDate | 2023-11-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Control Theory & Applications |
| spelling | doaj.art-94489e3319264bd0a2ca4537e02d332b2023-11-01T10:38:57ZengWileyIET Control Theory & Applications1751-86441751-86522023-11-0117162124213510.1049/cth2.12481Identification and location method of strip ingot for autonomous robot system using kmeans clustering and color segmentationQiguang Li0Huazheng Zheng1Tianwei Cui2Yumeng Zhang3School of Mechanical and Electrical Engineering Beijing Information Science and Technology University Beijing ChinaSchool of Mechanical and Electrical Engineering Beijing Information Science and Technology University Beijing ChinaSchool of Mechanical and Electrical Engineering Beijing Information Science and Technology University Beijing ChinaSchool of Mechanical and Electrical Engineering Beijing Information Science and Technology University Beijing ChinaAbstract In order to improve the efficiency of autonomous robot sorting steel ingots, this paper proposes a two‐dimensional weighted equivalent clustering‐based progressive probabilistic hough transform (2D‐WEC‐PPHT) algorithm for the problem of identifying and locating strip ingots in automatic picking strip ingot palletizing. First, the steel ingot image is preprocessed and the PPHT linear detection method is used to extract the edge linear information of the steel ingot. Second, the angle and position information are normalized and weighted, and a two‐dimensional clustering distance calculation method is proposed for two‐dimensional clustering of the extracted line information. Besides, the clustering line clusters with close angles were processed into equivalent lines by mean fitting. Then, the average threshold method was used to segment the ingot between each two fitting lines, and the minimum rectangle was used to box the segmentation part. The centroid of the box‐selected rectangle is the positioning center of the ingot, and the long side angle of the box‐selected rectangle is the deviation angle information of the ingot. Finally, the experimental results show that the number of redundant lines detected by the 2D‐WEC‐PPHT is significantly less than that of traditional methods such as HT and PPHT. The positioning speed of ingot is faster than that of HT and PPHT, and the processing time is reduced from 20 s to 10 s. In the case of large proportion of old ingots with weak reflection, the recognition accuracy reached 93.5%, and the angular and position positioning accuracy were 2.270° and 11.675 mm, respectively. The recognition accuracy of new ingots with strong reflection reached 99.574%, which met the requirements of picking positioning accuracy.https://doi.org/10.1049/cth2.12481hough transformsposition measurementrobot vision |
| spellingShingle | Qiguang Li Huazheng Zheng Tianwei Cui Yumeng Zhang Identification and location method of strip ingot for autonomous robot system using kmeans clustering and color segmentation IET Control Theory & Applications hough transforms position measurement robot vision |
| title | Identification and location method of strip ingot for autonomous robot system using kmeans clustering and color segmentation |
| title_full | Identification and location method of strip ingot for autonomous robot system using kmeans clustering and color segmentation |
| title_fullStr | Identification and location method of strip ingot for autonomous robot system using kmeans clustering and color segmentation |
| title_full_unstemmed | Identification and location method of strip ingot for autonomous robot system using kmeans clustering and color segmentation |
| title_short | Identification and location method of strip ingot for autonomous robot system using kmeans clustering and color segmentation |
| title_sort | identification and location method of strip ingot for autonomous robot system using kmeans clustering and color segmentation |
| topic | hough transforms position measurement robot vision |
| url | https://doi.org/10.1049/cth2.12481 |
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