원문정보
한국차세대컴퓨팅학회
한국차세대컴퓨팅학회 학술대회
The 9th International Conference on Next Generation Computing 2023
2023.12
pp.246-248
피인용수 : 0건 (자료제공 : 네이버학술정보)
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
I. INTRODUCTION
II. RELATED WORKS
III. METHODS
A. DATASET
B. YOLOv5 Model
C. Experiment setup
D. Evaluation Matrics
IV. EXPERIMENT RESULTS
V. CONCLUSIONS
REFERENCES
저자정보
참고문헌
자료제공 : 네이버학술정보