Abstract
[Objective] To address the low speed and efficiency as well as missed and false detection in manual inspection of defective products in pre -fried potato chip production, enhance the accuracy and speed of product defect identification, and ensure safe production. [Methods] An improved recognition algorithm, EISW -YOLOv 8n, based on YOLOv 8n is proposed.Firstly, the efficient multiscale channel attention (EMCA ) mechanism is introduced into the network to highlight important channel information.Secondly, to improve the model ability to extract features and capture long -distance dependencies within features, the iRMBS module, optimized by SWC convolution, is introduced into the C 2f module.Finally, the loss function WIOU is introduced to enhance the localization accuracy of the prediction box and the convergence speed of the model. [Results] The proposed model achieves the average precision of 94.3% for defect detection in pre -fried potato chips.Compared with the original YOLOv 8n model and common object detection algorithms, this network demonstrates superior performance. [Conclusion] EISW -YOLOv 8n can meet the requirements for identifying appearance defects in pre -fried potato chips.
Publication Date
12-11-2025
First Page
84
Last Page
90
DOI
10.13652/j.spjx.1003.5788.2025.80003
Recommended Citation
Jianjia, LI; Yongqiang, WANG; Congling, ZHOU; and Hao, WANG
(2025)
"An appearance defect detection method for pre-fried potato chips based on EISW-YOLOv 8n,"
Food and Machinery: Vol. 41:
Iss.
11, Article 12.
DOI: 10.13652/j.spjx.1003.5788.2025.80003
Available at:
https://www.ifoodmm.cn/journal/vol41/iss11/12
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