[연구] 석사과정 백승효, SCIE 논문지(MDPI Processes/Q2) 게재
- 스마트팩토리융합학과
- 조회수3153
- 2023-07-27
석사과정 백승효 학생(지도교수 : 정종필)의 연구(YOLOv7-Based Anomaly Detection Using Intensity and NG Types in Labeling in Cosmetic Manufacturing Processes)가 MDPI Processes(Impact Factor: 3.5 (2022); 5-Year Impact Factor: 3.4 (2022))에 게재됐다.
https://doi.org/10.3390/pr11082266 / https://www.mdpi.com/2227-9717/11/8/2266
논문요약 - The advent of the Fourth Industrial Revolution has revolutionized the manufacturing sector by integrating artificial intelligence into vision inspection systems to improve the efficiency and quality of products. Supervised-learning-based vision inspection systems have emerged as a powerful tool for automated quality control in various industries. During visual inspection or final inspection, a human operator physically inspects a product to determine its condition and categorize it based on their know-how. However, the know-how-based visual inspection process is limited in time and space and is affected by many factors. High accuracy in vision inspection is highly dependent on the quality and precision of the labeling process. Therefore, supervised learning methods of 1-STAGE DETECTION, such as You Only Look Once (YOLO), are utilized in automated inspection to improve accuracy. In this paper, we proposed a labeling method that achieves the highest inspection accuracy among labeling methods such as NG intensity and NG intensity when performing anomaly detection using YOLOv7 in the cosmetics manufacturing process.