YOLOv7-Based Anomaly Detection Using Intensity and NG Types in Labeling in Cosmetic Manufacturing Processes

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...

Full description

Saved in:
Bibliographic Details
Published in:Processes 2023-08, Vol.11 (8), p.2266
Main Authors: Beak, Seunghyo, Han, Yo-Han, Moon, Yeeun, Lee, Jieun, Jeong, Jongpil
Format: Article
Language:English
Subjects:
Anomalies
Artificial intelligence
Automatic control
Cosmetics
Cosmetics industry
COVID-19
Datasets
Factories
Industry 4.0
Injection molding
Inspection
Labeling
Manufacturing
Manufacturing industry
Pandemics
Quality control
Quality control equipment
Quality management
Supervised learning
Vision
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2227-9717
2227-9717
DOI:10.3390/pr11082266