A Fast Deployable Instance Elimination Segmentation Algorithm Based on Watershed Transform for Dense Cereal Grain Images

Junling Liang, Heng Li (), Fei Xu, Jianpin Chen, Meixuan Zhou, Liping Yin, Zhenzhen Zhai and Xinyu Chai ()
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Junling Liang: School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Heng Li: School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Fei Xu: Technical Center for Animal Plant and Food Inspection and Quarantine of Shanghai Customs, Shanghai 200002, China
Jianpin Chen: School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Meixuan Zhou: School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Liping Yin: Technical Center for Animal Plant and Food Inspection and Quarantine of Shanghai Customs, Shanghai 200002, China
Zhenzhen Zhai: Network & Information Center, Shanghai Jiao Tong University, Shanghai 200240, China
Xinyu Chai: School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China

Agriculture, 2022, vol. 12, issue 9, 1-14

Abstract: Cereal grains are a vital part of the human diet. The appearance quality and size distribution of cereal grains play major roles as deciders or indicators of market acceptability, storage stability, and breeding. Computer vision is popular in completing quality assessment and size analysis tasks, in which an accurate instance segmentation is a key step to guaranteeing the smooth completion of tasks. This study proposes a fast deployable instance segmentation method based on a generative marker-based watershed segmentation algorithm, which combines two strategies (one strategy for optimizing kernel areas and another for comprehensive segmentation) to overcome the problems of over-segmentation and under-segmentation for images with dense and small targets. Results show that the average segmentation accuracy of our method reaches 98.73%, which is significantly higher than the marker-based watershed segmentation algorithm (82.98%). To further verify the engineering practicality of our method, we count the size distribution of segmented cereal grains. The results keep a high degree of consistency with the manually sketched ground truth. Moreover, our proposed algorithm framework can be used as a great reference in other segmentation tasks of dense targets.

Keywords: cereal grain image; dense objects; elimination segmentation; watershed transform (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: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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