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A Method of Polished Rice Image Segmentation Based on YO-LACTS for Quality Detection

Author

Listed:
  • Jinbo Zhou

    (School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan 430023, China)

  • Shan Zeng

    (School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan 430023, China)

  • Yulong Chen

    (College of Medicine and Health Science, Wuhan Polytechnic University, Wuhan 430023, China)

  • Zhen Kang

    (School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan 430023, China)

  • Hao Li

    (School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan 430023, China)

  • Zhongyin Sheng

    (School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan 430023, China)

Abstract

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.

Suggested Citation

  • Jinbo Zhou & Shan Zeng & Yulong Chen & Zhen Kang & Hao Li & Zhongyin Sheng, 2023. "A Method of Polished Rice Image Segmentation Based on YO-LACTS for Quality Detection," Agriculture, MDPI, vol. 13(1), pages 1-16, January.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:1:p:182-:d:1032108
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    References listed on IDEAS

    as
    1. Bing Li & Bin Liu & Shuofeng Li & Haiming Liu, 2022. "An Improved EfficientNet for Rice Germ Integrity Classification and Recognition," Agriculture, MDPI, vol. 12(6), pages 1-16, June.
    2. Jinzhu Lu & Juncheng Xiang & Ting Liu & Zongmei Gao & Min Liao, 2022. "Sichuan Pepper Recognition in Complex Environments: A Comparison Study of Traditional Segmentation versus Deep Learning Methods," Agriculture, MDPI, vol. 12(10), pages 1-16, October.
    3. Junling Liang & Heng Li & Fei Xu & Jianpin Chen & Meixuan Zhou & Liping Yin & Zhenzhen Zhai & Xinyu Chai, 2022. "A Fast Deployable Instance Elimination Segmentation Algorithm Based on Watershed Transform for Dense Cereal Grain Images," Agriculture, MDPI, vol. 12(9), pages 1-14, September.
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    Cited by:

    1. Ange Lu & Lingzhi Ma & Hao Cui & Jun Liu & Qiucheng Ma, 2023. "Instance Segmentation of Lotus Pods and Stalks in Unstructured Planting Environment Based on Improved YOLOv5," Agriculture, MDPI, vol. 13(8), pages 1-22, August.

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    More about this item

    Keywords

    polished rice; RoI; YOLOv5; YOLACT;
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