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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- Korean Society for Precision Engineering Journal of the Korean Society for Precision Engineering Journal of the Korean Society for Precision Engineering Vol.41 No.4
- 발행연도
- 2024.4
- 수록면
- 279 - 286 (8page)
- DOI
- 10.7736/JKSPE.023.147
이용수
초록· 키워드
오류제보하기
This study developed a defect-detecting system for automotive wheel nuts. We proposed an image processing method using OpenCV for efficient defect-detection of automotive wheel nuts. Image processing method focused on noise removal, ratio adjustment, binarization, polar coordinate system formation, and orthogonal coordinate system conversion. Through data collection, preprocessing, object detection model training, and testing, we established a system capable of accurately classifying defects and tracking their positions. There are four defect types. Types 1 and 2 defects are defects of products where the product is completely broken circumferentially. Types 3 and 4 defects are defects are small circumferential dents and scratches in the product. We utilized Faster R-CNN and YOLOv8 models to detect defect types. By employing effective preprocessing and post-processing steps, we enhanced the accuracy. In the case of Fast RCNN, AP values were 0.92, 0.93, 0.76, and 0.49 for types 1, 2, 3, and 4 defects, respectively. The mAP was 0.77. In the case of YOLOv8, AP values were 0.78, 0.96, 0.8, and 0.51 for types for types 1, 2, 3, and 4 defects, respectively. The mAP was 0.76. These results could contribute to defect detection and quality improvement in the automotive manufacturing sector.
목차
1. 서론
2. 객체 탐지를 통한 불량 검출
3. 객체 검출을 통한 불량 검출
4. 고찰
5. 결론
REFERENCES
참고문헌 (12)
참고문헌 신청
Kim, D., Kong, J., Lim, J., Sho, B., (2020), A study on data collection and object detection using faster R-CNN for application to construction site safety, Journal of the Korean Society of Hazard Mitigation, 20(1), 119-126.
Yu, S., Lee, H., Park, T., Yoon, J., Seo, K., (2021), A comparative study on the performance of deep learning model for small defects in metal precision casting products, Proceedings of the Human Computer Interaction Korea 2022, 677-681.
Lee, M.-R., Yasir, S. M., Ahn, H., (2022), Defect detection of motion blurred metal surface images using YOLOv5, Proceedings of the Journal of the Institute of Electronics and Information Engineers, 2267-2268.
Su, Y., Yan, P., (2020), A defect detection method of gear end-face based on modified YOLO-v3, Proceedings of the 2020 10th Institute of Electrical and Electronics Engineers International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER), 283-288.
Zhou, H., Jang, B., Chen, Y., Troendle, D., (2020), Exploring faster R-CNN for fabric defect detection, Proceedings of the 2020 Third International Conference on Artificial Intelligence for Industries (AI4I), 52-55.
Yu, S., Lee, H., Park, T., Yoon, J., Seo, K., (2021), A comparative study on the performance of deep learning model for small defects in metal precision casting products, Proceedings of the Human Computer Interaction Korea 2022, 677-681.
Lee, M.-R., Yasir, S. M., Ahn, H., (2022), Defect detection of motion blurred metal surface images using YOLOv5, Proceedings of the Journal of the Institute of Electronics and Information Engineers, 2267-2268.
Su, Y., Yan, P., (2020), A defect detection method of gear end-face based on modified YOLO-v3, Proceedings of the 2020 10th Institute of Electrical and Electronics Engineers International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER), 283-288.
Zhou, H., Jang, B., Chen, Y., Troendle, D., (2020), Exploring faster R-CNN for fabric defect detection, Proceedings of the 2020 Third International Conference on Artificial Intelligence for Industries (AI4I), 52-55.
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