Defect Detection in the Forging Process of Wheel Nut Products through Object Detection

Author:

Kim Chang Dae,Baek Seung Wook,Chung Wan Jjin,Lee Chang WhanORCID

Funder

Seoul National University of Science and Technology

Publisher

Korean Society of Precision Engineering

Reference12 articles.

1. A Study on Data Collection and Object Detection using Faster R-CNN for Application to Construction Site Safety

2. 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.

3. 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.

4. A defect detection method of gear end-face based on modified YOLO-V3

5. Exploring Faster RCNN for Fabric Defect Detection

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