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Inspection of Heat Seal Packing Bag Integrity Using Thermal Images With YOLO Algorithm
Author(s)
Date Issued
January 1, 2025
Type
Conference Paper
Abstract
This paper presents an inspection of heat seal packing bag integrity using thermal imaging with a deep learning technique. The performance was evaluated by comparing the object detection rates obtained from the YOLOv4-Tiny Algorithm and the Convolutional Neural Network (CNN) technique. Two sets of completely sealed and failure-sealed packaging bags were prepared for the training (100 bags) and testing (100 bags) of the models. Some sample bags containing tomato sauce inserted between the seals represent a failure-sealed condition. The integrity of the heat seal packaging bag was analyzed by examining the differences in color shade patterns of the thermal images. As a result, the YOLOv4-Tiny model achieved a detection accuracy of 98.80%, significantly outperforming the CNN technique, which detected only 78.40%, while using the same dataset; additionally, the time required for detection and training was faster.
Citation
2025 10th International Conference on Business and Industrial Research Advanced Technology and Innovation for Sustainable Society Icbir 2025 Proceedings, 197-201, 2025