Improved Real-Time Models for Object Detection and Instance Segmentation for Agaricus bisporus Segmentation and Localization System Using RGB-D Panoramic Stitching Images

Chenbo Shi, Yuanzheng Mo, Xiangqun Ren, Jiahao Nie, Chun Zhang, Jin Yuan and Changsheng Zhu ()
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Chenbo Shi: College of Intelligent Equipment, Shandong University of Science and Technology, Tai’an 271019, China
Yuanzheng Mo: College of Intelligent Equipment, Shandong University of Science and Technology, Tai’an 271019, China
Xiangqun Ren: College of Intelligent Equipment, Shandong University of Science and Technology, Tai’an 271019, China
Jiahao Nie: College of Intelligent Equipment, Shandong University of Science and Technology, Tai’an 271019, China
Chun Zhang: College of Intelligent Equipment, Shandong University of Science and Technology, Tai’an 271019, China
Jin Yuan: College of Mechanical and Electronic Engineering, Shandong Agricultural University, Tai’an 271018, China
Changsheng Zhu: College of Intelligent Equipment, Shandong University of Science and Technology, Tai’an 271019, China

Agriculture, 2024, vol. 14, issue 5, 1-18

Abstract: The segmentation and localization of Agaricus bisporus is a precondition for its automatic harvesting. A. bisporus growth clusters can present challenges for precise localization and segmentation because of adhesion and overlapping. A low-cost image stitching system is presented in this research, utilizing a quick stitching method with disparity correction to produce high-precision panoramic dual-modal fusion images. An enhanced technique called Real-Time Models for Object Detection and Instance Segmentation (RTMDet-Ins) is suggested. This approach utilizes SimAM Attention Module’s (SimAM) global attention mechanism and the lightweight feature fusion module Space-to-depth Progressive Asymmetric Feature Pyramid Network (SPD-PAFPN) to improve the detection capabilities for hidden A. bisporus . It efficiently deals with challenges related to intricate segmentation and inaccurate localization in complex obstacles and adhesion scenarios. The technology has been verified by 96 data sets collected on a self-designed fully automatic harvesting robot platform. Statistical analysis shows that the worldwide stitching error is below 2 mm in the area of 1200 mm × 400 mm. The segmentation method demonstrates an overall precision of 98.64%. The planar mean positioning error is merely 0.31%. The method promoted in this research demonstrates improved segmentation and localization accuracy in a challenging harvesting setting, enabling efficient autonomous harvesting of A. bisporus .

Keywords: automatic picking robot; mushroom detection; attention mechanism; image processing; computer vision (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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