Improved Real-Time Models for Object Detection and Instance Segmentation for <i>Agaricus bisporus</i> Segmentation and Localization System Using RGB-D Panoramic Stitching Images
Chenbo Shi | Yuanzheng Mo | Xiangqun Ren | Jiahao Nie | Chun Zhang | Jin Yuan | Changsheng Zhu
The segmentation and localization of <i>Agaricus bisporus</i> is a precondition for its automatic harvesting. <i>A. bisporus</i> 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 <i>A. bisporus</i>. 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 <i>A. bisporus</i>.
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