A Method of Polished Rice Image Segmentation Based on YO-LACTS for Quality Detection
The problem of small and multi-object polished rice image segmentation has always been one of importance and difficulty in the field of image segmentation. In the appearance quality detection of polished rice, image segmentation is a crucial part, directly affecting the results of follow-up physicoc...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Agriculture |
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| Online Access: | https://www.mdpi.com/2077-0472/13/1/182 |
| _version_ | 1797447349986918400 |
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| author | Jinbo Zhou Shan Zeng Yulong Chen Zhen Kang Hao Li Zhongyin Sheng |
| author_facet | Jinbo Zhou Shan Zeng Yulong Chen Zhen Kang Hao Li Zhongyin Sheng |
| author_sort | Jinbo Zhou |
| collection | DOAJ |
| description | The problem of small and multi-object polished rice image segmentation has always been one of importance and difficulty in the field of image segmentation. In the appearance quality detection of polished rice, image segmentation is a crucial part, directly affecting the results of follow-up physicochemical indicators. To avoid leak detection and inaccuracy in image segmentation qualifying polished rice, this paper proposes a new image segmentation method (YO-LACTS), combining YOLOv5 with YOLACT. We tested the YOLOv5-based object detection network, to extract Regions of Interest (RoI) from the whole image of the polished rice, in order to reduce the image complexity and maximize the target feature difference. We refined the segmentation of the RoI image by establishing the instance segmentation network YOLACT, and we eventually procured the outcome by merging the RoI. Compared to other algorithms based on polished rice datasets, this constructed method was shown to present the image segmentation, enabling researchers to evaluate polished rice satisfactorily. |
| first_indexed | 2024-03-09T13:53:40Z |
| format | Article |
| id | doaj.art-fecafa4a6c69497a85bb1f7c04e3f1d5 |
| institution | Directory Open Access Journal |
| issn | 2077-0472 |
| language | English |
| last_indexed | 2024-03-09T13:53:40Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj.art-fecafa4a6c69497a85bb1f7c04e3f1d52023-11-30T20:46:56ZengMDPI AGAgriculture2077-04722023-01-0113118210.3390/agriculture13010182A Method of Polished Rice Image Segmentation Based on YO-LACTS for Quality DetectionJinbo Zhou0Shan Zeng1Yulong Chen2Zhen Kang3Hao Li4Zhongyin Sheng5School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan 430023, ChinaSchool of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan 430023, ChinaCollege of Medicine and Health Science, Wuhan Polytechnic University, Wuhan 430023, ChinaSchool of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan 430023, ChinaSchool of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan 430023, ChinaSchool of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan 430023, ChinaThe problem of small and multi-object polished rice image segmentation has always been one of importance and difficulty in the field of image segmentation. In the appearance quality detection of polished rice, image segmentation is a crucial part, directly affecting the results of follow-up physicochemical indicators. To avoid leak detection and inaccuracy in image segmentation qualifying polished rice, this paper proposes a new image segmentation method (YO-LACTS), combining YOLOv5 with YOLACT. We tested the YOLOv5-based object detection network, to extract Regions of Interest (RoI) from the whole image of the polished rice, in order to reduce the image complexity and maximize the target feature difference. We refined the segmentation of the RoI image by establishing the instance segmentation network YOLACT, and we eventually procured the outcome by merging the RoI. Compared to other algorithms based on polished rice datasets, this constructed method was shown to present the image segmentation, enabling researchers to evaluate polished rice satisfactorily.https://www.mdpi.com/2077-0472/13/1/182polished riceRoIYOLOv5YOLACT |
| spellingShingle | Jinbo Zhou Shan Zeng Yulong Chen Zhen Kang Hao Li Zhongyin Sheng A Method of Polished Rice Image Segmentation Based on YO-LACTS for Quality Detection Agriculture polished rice RoI YOLOv5 YOLACT |
| title | A Method of Polished Rice Image Segmentation Based on YO-LACTS for Quality Detection |
| title_full | A Method of Polished Rice Image Segmentation Based on YO-LACTS for Quality Detection |
| title_fullStr | A Method of Polished Rice Image Segmentation Based on YO-LACTS for Quality Detection |
| title_full_unstemmed | A Method of Polished Rice Image Segmentation Based on YO-LACTS for Quality Detection |
| title_short | A Method of Polished Rice Image Segmentation Based on YO-LACTS for Quality Detection |
| title_sort | method of polished rice image segmentation based on yo lacts for quality detection |
| topic | polished rice RoI YOLOv5 YOLACT |
| url | https://www.mdpi.com/2077-0472/13/1/182 |
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