Fig 1.
Overview of the present research.
Table 1.
Related works.
Fig 2.
YOLOv5 architecture diagram.
Fig 3.
CBAM module.
Fig 4.
FPN+ PANet network.
Fig 5.
Network optimization diagram.
Fig 6.
Suggested ML model in this study.
Fig 7.
Camera.
Fig 8.
Arduino MCU.
Fig 9.
Load cell.
Fig 10.
Moisture sensor.
Table 2.
Hardware and software configuration.
Table 3.
Dataset used in this study.
Fig 11.
Input image.
Table 4.
Setting parameters of optimized model in the training stage.
Fig 12.
Training loss plot.
Table 5.
Performance comparison of each model on the test dataset.
Table 6.
Comparative results of Ablation experiments.
Fig 13.
Detection diagram.
Table 7.
Quantitative performance of various ML models for food packaging defect detection.