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

Jinbo Zhou, Shan Zeng (), Yulong Chen, Zhen Kang, Hao Li and Zhongyin Sheng
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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

Agriculture, 2023, vol. 13, issue 1, 1-16

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.

Keywords: polished rice; RoI; YOLOv5; YOLACT (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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