@inproceedings{ae5c8b862dd64080b210400bac89822d,
title = "AI-Enhanced Visual Inspection Systems for Robust Detection of Product Packaging Defects in Manufacturing Environments",
abstract = "AI-enhanced visual inspection systems are important for ensuring product quality and customer satisfaction, particularly in automated manufacturing environments where defects can disrupt supply chains and lead to contamination risks. This study applies the YOLOv7 object detection algorithm for detecting packaging defects such as scratches holes, and deformations in jar lids. The dataset contains 1859 jar lids, on average 11 per image, categorized into intact (962) versus damaged (897) jar lids. After optimizing for class imbalance and performance, the system achieved precision rates of 91.7 % overall and 3.5 % for the 'damaged' class, demonstrating robustness in challenging environments. This work presents a scalable AI-based solution to improve defect detection efficiency in manufacturing.",
keywords = "Deep Learning, Defect Detection, Machine Learning, Object Detection",
author = "Imran Ahmed and Siddiqi, {Muftooh Ur Rehman} and Misbah Ahmad",
year = "2024",
month = nov,
day = "20",
doi = "10.1109/uemcon62879.2024.10754674",
language = "English",
series = "Proceedings from Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON)",
publisher = "IEEE",
editor = "Rajashree Paul and Arpita Kundu",
booktitle = "2024 IEEE 15th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON)",
address = "United States",
note = "15th IEEE Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2024 ; Conference date: 17-10-2024 Through 19-10-2024",
}