A High-Accuracy Contour Segmentation and Reconstruction of a Dense Cluster of Mushrooms Based on Improved SOLOv2

Shuzhen Yang (), Jingmin Zhang and Jin Yuan
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Shuzhen Yang: School of Intelligent Manufacturing and Control Engineering, Shanghai Polytechnic University, Shanghai 201209, China
Jingmin Zhang: School of Intelligent Manufacturing and Control Engineering, Shanghai Polytechnic University, Shanghai 201209, China
Jin Yuan: College of Mechanical and Electronic Engineering, Shandong Agricultural University, Taian 271018, China

Agriculture, 2024, vol. 14, issue 9, 1-25

Abstract: This study addresses challenges related to imprecise edge segmentation and low center point accuracy, particularly when mushrooms are heavily occluded or deformed within dense clusters. A high-precision mushroom contour segmentation algorithm is proposed that builds upon the improved SOLOv2, along with a contour reconstruction method using instance segmentation masks. The enhanced segmentation algorithm, PR-SOLOv2, incorporates the PointRend module during the up-sampling stage, introducing fine features and enhancing segmentation details. This addresses the difficulty of accurately segmenting densely overlapping mushrooms. Furthermore, a contour reconstruction method based on the PR-SOLOv2 instance segmentation mask is presented. This approach accurately segments mushrooms, extracts individual mushroom masks and their contour data, and classifies reconstruction contours based on average curvature and length. Regular contours are fitted using least-squares ellipses, while irregular ones are reconstructed by extracting the longest sub-contour from the original irregular contour based on its corners. Experimental results demonstrate strong generalization and superior performance in contour segmentation and reconstruction, particularly for densely clustered mushrooms in complex environments. The proposed approach achieves a 93.04% segmentation accuracy and a 98.13% successful segmentation rate, surpassing Mask RCNN and YOLACT by approximately 10%. The center point positioning accuracy of mushrooms is 0.3%. This method better meets the high positioning requirements for efficient and non-destructive picking of densely clustered mushrooms.

Keywords: contour reconstruction; densely overlapping mushrooms; high-precision contour segmentation; image segmentation; SOLOV2 (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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