Earticle

초록

영어

The phenotypic characteristics of plants, including their length and width, are key indicators for evaluating growth status. In this study, we propose a robust framework for radish phenotype evaluation based on an improved SOLOv2 instance segmentation algorithm and a dataset of 1100 annotated images. The enhanced model enables precise segmentation of radish components, facilitating accurate measurement of leaf and root size. Furthermore, we integrate a Channel–Spatial Attention Module (CSAM) into the feature extraction stage to optimize the backbone, and incorporate soft attention mechanisms into the Feature Pyramid Network (FPN) to enhance its representation capability. Experimental evaluations show that the improved SOLOv2 model achieves an average segmentation accuracy of 94.3%. The proposed system significantly reduces the labor and time required by traditional measurement methods.

목차

Abstract
I. INTRODUCTION
II. RELATED WORK
III. METHODOLOGIES
A. Backbone
B. FPN with the soft-attention module
IV. EXPERIMENTS
A. Dataset
B. Compare With the Other Model
C. Robust Radish Segmentation Analysis
V. CONCLUSION
ACKNOWLEDGMENT
REFERENCES

저자

• Wenqi Zhang [ Department of Computer Science & Engineering Sejong University Seoul,Republic of Korea ]
• Sufyan Danish [ Department of Computer Science & Engineering Sejong University Seoul,Republic of Korea ]
• L. Minh Dang [ Department of Computer Science & Engineering Sejong University Seoul,Republic of Korea ]
• Yue Zhang [ Department of Computer Science & Engineering Sejong University Seoul,Republic of Korea ]
• Yanan Wang [ Department of Computer Science & Engineering Sejong University Seoul,Republic of Korea ]
• Seongwook Lee [ Department of Artificial Intelligence Data Science Sejong University Seoul,Republic of Korea ]
• Hyeonjoon Moon [ Department of Computer Science & Engineering Sejong University Seoul,Republic of Korea ]

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