计算机与现代化Issue(9)：35-42,8.DOI:10.3969/j.issn.1006-2475.2025.09.005
改进YOLOv8的轻量化垃圾分类检测
Lightweight Garbage Classification and Detection of Improved YOLOv8
摘要
Abstract
The current garbage classification and detection algorithms based on deep learning often have a large number of model parameters,leading to increased storage and computing costs.This results in significant computational load when running on resource-constrained mobile devices.To solve the above problems,a lightweight garbage detection algorithm based on improved YOLOv8n is proposed.The improved algorithm uses the GhostNet convolution module to realize the lightweight network in the YO-LOv8n feature extraction network module.The RepConv structure reparameterization is used to improve the backbone network,which enhances the backbone network's feature extraction ability and reduces its complexity during inference stages.Additionally,the C2f module of the neck network is improved by using convolution kernels of different sizes to obtain multi-scale feature informa-tion,thereby enhancing the detection accuracy of the model.Finally,transfer learning is used to improve generalization capabili-ties while accelerating model training for better overall detection accuracy.The experimental results show that the improved algo-rithm reduces both parameter count and computation by 26.8%and 24.7%,respectively,compared with the original model while achieving average detection accuracies of mAP50 and mAP50:95 at 98.1%and 93.8%.Overall,the proposed method not only re-duces model complexity but also has better detection accuracy and can better adapt to the requirements of mobile devices.关键词
垃圾分类检测/轻量化网络/Ghost卷积/结构重参数/多尺度特征融合Key words
garbage classification and detection/lightweight network/Ghost convlution/structure reparameterization/multi-scale feature fusion分类
信息技术与安全科学引用本文复制引用
罗德艳,徐杨,左锋云,张永丹..改进YOLOv8的轻量化垃圾分类检测[J].计算机与现代化,2025,(9):35-42,8.基金项目
贵州省科技计划项目(黔科合支撑[2023]一般326) （黔科合支撑[2023]一般326）
