包装与食品机械2025，Vol.43Issue(1)：32-39,8.DOI:10.3969/j.issn.1005-1295.2025.01.005
饮料包装缺陷检测的轻量化算法研究
Research on lightweight algorithms for beverage packaging defect detection
摘要
Abstract
To address the challenges of low accuracy and slow inference speed in defect detection on beverage packaging production lines,this study proposes a lightweight defect detection algorithm named Light YOLOv8-DP,an improved YOLOv8-based framework.The algorithm enhances small object detection accuracy while reducing computational resource consumption.Specifically,in the YOLOv8 backbone network,the original C2f module is replaced with the RepStreamGhost(RSG)module to optimize gradient propagation.The neck network is redesigned as a Small Target Boost Pyramid(STBP)structure integrated with an AdaptiveOK(AOK)module to strengthen multi-scale feature representation.Additionally,a Reparameterized Shared Convolutional Detection(RSCD)head is introduced to minimize parameter size and computational complexity.Experimental validation was performed on a custom-built beverage packaging defect dataset.Results show that Light YOLOv8-DP achieves a mean average precision(mAP)of 85.5%,recall of 82.1%,and precision of 83.8%,representing improvements of 2.3%,3.3%,and 3.9%,respectively,over the baseline YOLOv8.The inference speed reaches 259.26 FPS(frames per second),43.65 FPS faster than the original model.Real-time validation further demonstrates a 1.7-point increase in F1-score and a 0.7 ms reduction in per-image processing latency.This research provides a novel approach for automated quality inspection in food packaging systems.关键词
YOLOv8/缺陷检测/RSG模块/AOK模块/RSCD检测头Key words
YOLOv8/defect detection/RSG module/AOK module/RSCD detection head分类
轻工业引用本文复制引用
付赫,王桂英..饮料包装缺陷检测的轻量化算法研究[J].包装与食品机械,2025,43(1):32-39,8.基金项目
国家自然科学基金项目(32071692) （32071692）
