Earticle

초록

영어

Weld defect inspection is essential to ensuring the safety of weld joints. However, this is a subjective, complex, and labor-intensive task for workers. To relieve this problem, this paper aims to weld defect detection tasks by applying the stateof- the-art YOLOv5x-seg by modifying the YOLOv5 network. In particular, we attempt to utilize the pixel-level polygon representation. Experimental results show that it achieves 82.6% mAP@0.5. In conclusion, our result shows that YOLOv5xseg can successfully perform weld defect detection tasks.

목차

Abstract
I. INTRODUCTION
II. RELATED WORKS
III. METHODS
A. DATASET
B. YOLOv5 Model
C. Experiment setup
D. Evaluation Matrics
IV. EXPERIMENT RESULTS
V. CONCLUSIONS
REFERENCES

저자

• Minsung Jung [ Dept.of Computer Science & Engineering Kyungnam University ]
• Yong Min Cho [ Dept.of Computer Science & Engineering Kyungnam University ]
• Yun Seok Choi [ Dept.of Computer Science & Engineering Kyungnam University ]
• Byung-Joo Shin [ Dept.of Computer Science & Engineering Kyungnam University ] Corresponding author

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