Abstract
［Objective］ To address the problems of low detection accuracy and poor real -time performance in traditional packaging defect detection methods in food production.［Methods］ Based on the analysis of automated food production lines,an improved YOLOv 10 model was applied for packaging defect detection on boxed food production lines.The convolutional block attention module (CBAM ) was introduced after the C 2f modules in the backbone and neck layers to enhance the model's feature localization ability in complex backgrounds.Full -dimensional dynamic convolution was introduced in the backbone and neck layers to reduce computational redundancy and improve detection accuracy.The P 2 layer was added and the P 5 layer removed in the head to enhance small object detection performance.The loss function in the head was optimized to improve the model's convergence performance.［Results］］ The proposed method effectively improved the detection accuracy of packaging defects in boxed food,met real -time performance requirements,achieved a detection accuracy greater than 98.00%,and an average detection time of less than 0.02 s.［Conclusion］ The integration of deep learning and machine vision can realize rapid and accurate detection of packaging defects in boxed food.
Publication Date
6-2-2025
First Page
236
Last Page
241
DOI
10.13652/j.spjx.1003.5788.2025.60008
Recommended Citation
Jinglei, WANG; Yingjie, ZHAO; Feng, LI; and Chunyu, WANG
(2025)
"Packaging defect detection method for boxed food production line based on improved YOLOv 10,"
Food and Machinery: Vol. 41:
Iss.
4, Article 29.
DOI: 10.13652/j.spjx.1003.5788.2025.60008
Available at:
https://www.ifoodmm.cn/journal/vol41/iss4/29
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