包装与食品机械2025，Vol.43Issue(4)：1-12,12.DOI:10.3969/j.issn.1005-1295.2025.04.001
基于YOLOv8n改进的轻量化酒品包装缺陷检测算法
Improved lightweight algorithm for liquor packaging defect detection based on YOLOv8n
摘要
Abstract
To address the issues of low accuracy,slow speed,and high complexity leading to poor deployability of liquor packaging detection algorithms in quality inspection tasks,an improved lightweight algorithm based on YOLOv8n is proposed.An average pooling branch was added to the SPPF module in the backbone network,the structure of the EMA attention mechanism was reinforced,and a large-scale convolutional branch with variable kernels was expanded and embedded into the SPPF module as the output.Combining ADown and HWD downsampling methods,a new downsampling module was designed to reduce redundant parameters while preserving richer feature information,enhancing the model's feature representation capability.A convolutional weight-sharing strategy was adopted to lightweight the detection head,and a module combining depthwise separable convolution and grouped convolution was used to further reduce model complexity.The Focaler-PIoU loss function was employed to optimize localization loss and accelerate algorithm convergence.A self-made liquor packaging dataset was used for training and validation,and generalization testing was conducted on the publicly available Alibaba Cloud Tianchi dataset of flawed bottled liquor.Experimental results show that compared to the baseline YOLOv8n model,the improved algorithm increased mAP50 and mAP50-95 by 3.5 and 4.8 percentage points,respectively,while reducing parameter count and computational load by 33.3%and 37.0%.On the publicly available flawed bottled liquor dataset,the improved algorithm increased mAP50 and mAP50-95 by 1.5 and 1.1 percentage points,respectively,demonstrating its strong generalization capability.This research provides theoretical support for quality inspection of liquor packaging.关键词
包装检测/缺陷检测/深度学习/YOLOv8/轻量化/注意力机制Key words
packaging inspection/defect detection/deep learning/YOLOv8/lightweight/attention mechanism分类
通用工业技术引用本文复制引用
向硕,曾水玲,贺刚健,林方聪..基于YOLOv8n改进的轻量化酒品包装缺陷检测算法[J].包装与食品机械,2025,43(4):1-12,12.基金项目
国家自然科学基金项目(61966014) （61966014）
湖南省自然科学基金项目(2024JJ7413) （2024JJ7413）
湖南省研究生科研创新项目(QL20230255,CX20221107) （QL20230255,CX20221107）
吉首大学科研项目(JGY2023071,JDX202409,JDX202420) （JGY2023071,JDX202409,JDX202420）
