Sorting of Mountage Cocoons Based on MobileSAM and Target Detection

Mochen Liu, Mingshi Cui, Wei Wei, Xiaoli Xu, Chongkai Sun, Fade Li, Zhanhua Song, Yao Lu, Ji Zhang, Fuyang Tian, Guizheng Zhang and Yinfa Yan ()
Additional contact information
Mochen Liu: College of Mechanical and Electrical Engineering, Shandong Agriculture University, Tai’an 271018, China
Mingshi Cui: College of Mechanical and Electrical Engineering, Shandong Agriculture University, Tai’an 271018, China
Wei Wei: Sericulture Technology Promotion Station of Guangxi Zhuang Autonomous Region, Nanning 530000, China
Xiaoli Xu: College of Mechanical and Electrical Engineering, Shandong Agriculture University, Tai’an 271018, China
Chongkai Sun: College of Mechanical and Electrical Engineering, Shandong Agriculture University, Tai’an 271018, China
Fade Li: College of Mechanical and Electrical Engineering, Shandong Agriculture University, Tai’an 271018, China
Zhanhua Song: College of Mechanical and Electrical Engineering, Shandong Agriculture University, Tai’an 271018, China
Yao Lu: College of Mechanical and Electrical Engineering, Shandong Agriculture University, Tai’an 271018, China
Ji Zhang: College of Mechanical and Electrical Engineering, Shandong Agriculture University, Tai’an 271018, China
Fuyang Tian: College of Mechanical and Electrical Engineering, Shandong Agriculture University, Tai’an 271018, China
Guizheng Zhang: Sericulture Technology Promotion Station of Guangxi Zhuang Autonomous Region, Nanning 530000, China
Yinfa Yan: College of Mechanical and Electrical Engineering, Shandong Agriculture University, Tai’an 271018, China

Agriculture, 2024, vol. 14, issue 4, 1-22

Abstract: The classification of silkworm cocoons is essential prior to silk reeling and serves as a key step in improving the quality of raw silk. At present, cocoon classification mainly relies on manual sorting, which is labor-intensive and inefficient. In this paper, a cocoon detection algorithm S-YOLOv8_c based on the cooperation of MobileSAM and YOLOv8 for the mountage cocoons was proposed. The MobileSAM with a designed area thresholding algorithm was used for the semantic segmentation of mountage cocoon images, which could mitigate the effect of complex backgrounds and maximize the discriminability of cocoon features. Subsequently, the BiFPN was added to the neck of YOLOv8 to improve the multiscale feature fusion capability. The loss function was replaced with the WIoU, and a dynamic non-monotonic focusing mechanism was introduced to improve the generalization ability. In addition, the GAM was incorporated into the head to focus on detailed cocoon information. Finally, the S-YOLOv8_c achieved a good detection accuracy on the test set, with a mAP of 95.8%. Furthermore, to experimentally validate the sorting ability, we deployed the proposed model onto the self-developed Cartesian coordinate automatic cocoon harvester, which indicated that it would effectively meet the requirements of accurate and efficient cocoon sorting.

Keywords: mountage cocoons; MobileSAM; YOLOv8; cocoon sorting (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: 2024
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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