Abstract
［Objective］ To improve the quality and yield of liquor by achieving efficient and accurate detection of alcohol content during the liquor gathering process and to develop an detection model of alcohol content in liquor gathering based on improved YOLOv 5s.［Methods］ This study replaces the original feature extraction module of the YOLOv 5s model with the lightweight ShuffleNetV 2 module to reduce the model's depth,making it more compact.The convolutional block attention module (CBAM ) dual -channel attention mechanism is added to the feature extraction process to capture features from different dimensions.The SIOU loss function is used to replace the original model's loss function.A novel method for alcohol content detection based on the improved YOLOv 5s model is proposed.［Results］］ The improved model achieves an accuracy of 91.9%,with a model size of 6.7 MB.The recall rate and mean average precision (mAP ) are 89.3% and 96.3%,respectively,showing an increase of 10.3% and 12.3% over the original YOLOv 5s model.Compared to current mainstream models like YOLOv 3,YOLOv 5m,and YOLOv 8,the mAP has increased by 44.3%,9.3%,and 13.1%,respectively.［Conclusion］ The improved YOLOv 5s model proposed in this paper provides high accuracy in detecting alcohol content during the liquor gathering.
Publication Date
7-11-2025
First Page
65
Last Page
71
DOI
10.13652/j.spjx.1003.5788.2024.80988
Recommended Citation
Shuang, WANG; Yuanxia, JI; Honggang, WU; Runling, YANG; and Shaokun, LU
(2025)
"Detection of alcohol content in liquor gathering based on improved YOLOv 5s,"
Food and Machinery: Vol. 41:
Iss.
7, Article 10.
DOI: 10.13652/j.spjx.1003.5788.2024.80988
Available at:
https://www.ifoodmm.cn/journal/vol41/iss7/10
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