计算机工程2025，Vol.51Issue(7)：140-151,12.DOI:10.19678/j.issn.1000-3428.0069395
基于视觉大模型的垃圾分类轻量化算法研究
Research on Lightweight Algorithm for Garbage Classification Based on Visual Large Model
摘要
Abstract
As deep learning technology progresses rapidly,it is being increasingly applied in garbage classification,thereby significantly improving classification accuracy and efficiency.However,practical application is hindered by many challenges,such as high data acquisition and annotation costs,insufficient model generalizability,and difficulty in meeting real-time requirements.To address these issues,this paper proposes LSM-PPLCNet,a lightweight garbage classification algorithm combining a large visual model with PP-LCNet.LSM-PPLCNet combines the powerful feature extraction capabilities of large visual models with the design of lightweight models,ensuring that the model meets real-time requirements while achieving improved accuracy on a self-made garbage classification dataset.First,a semi-supervised training strategy based on the CLIP large model is used for data mining on unlabeled data to enrich the training samples and reduce the cost of manual annotation.Second,the knowledge distillation method is used,with the high-precision CLIP large model serving as the teacher model to guide the training of the lightweight network.Finally,the loss function is optimized,and a weighted loss based on the large model is proposed.By assigning different proportions of the loss function to different images,the model can adjust the proportions in the loss function according to the different qualities of the images.After rigorous training and testing on a self-made garbage classification dataset,experimental results show that compared with the original PP-LCNet classification model,LSM-PPLCNet improves the Top-1 Accuracy by 4.03 percentage points without affecting the inference speed and has significant advantages compared with other mainstream models.These results show that LSM-PPLCNet can achieve high-precision and high-speed classification performance in garbage classification tasks.关键词
垃圾分类/视觉大模型/权重损失/半监督/知识蒸馏Key words
garbage classification/visual large model/weight loss/semi-supervised/knowledge distillation分类
信息技术与安全科学引用本文复制引用
张玉博,杨帆,郭亚,杨文慧..基于视觉大模型的垃圾分类轻量化算法研究[J].计算机工程,2025,51(7):140-151,12.基金项目
石家庄市科技合作专项重大项目(SJZZXA23005). （SJZZXA23005）
