用AI提升垃圾分类准确率，助力智慧城市可持续发展
AI-Enabled Waste Classification as a Data-Driven Decision Support Tool for Circular Economy and Urban Sustainability
- 对比多种机器学习与深度学习模型，优化垃圾分类识别
- DenseNet121达91%准确率，比传统模型高20个百分点
- 适合城市环保部门和智慧环卫系统开发者参考
高效垃圾分类对推动循环经济和资源回收至关重要。本文评估了随机森林、SVM、AdaBoost等传统机器学习方法，以及自定义CNN、VGG16、ResNet50和三种迁移学习模型（DenseNet121、EfficientNetB0、InceptionV3）在25,077张垃圾图像上的二分类表现（80/20训练测试划分，图像经增强并缩放至150x150像素）。研究分析了主成分分析（PCA）对传统模型的降维影响。结果表明，DenseNet121达到最高准确率（91%）和ROC-AUC（0.98），较最优传统分类器提升20个百分点。PCA对传统模型改善有限，而迁移学习在数据量受限时显著提升性能。最后，论文提出将这些模型集成到实时数据驱动决策支持系统中，可有效减少填埋量并降低全生命周期环境影响。
原文摘要 · Abstract (English)
Efficient waste sorting is crucial for enabling circular-economy practices and resource recovery in smart cities. This paper evaluates both traditional machine-learning (Random Forest, SVM, AdaBoost) and deep-learning techniques including custom CNNs, VGG16, ResNet50, and three transfer-learning models (DenseNet121, EfficientNetB0, InceptionV3) for binary classification of 25 077 waste images (80/20 train/test split, augmented and resized to 150x150 px). The paper assesses the impact of Principal Component Analysis for dimensionality reduction on traditional models. DenseNet121 achieved the highest accuracy (91 %) and ROC-AUC (0.98), outperforming the best traditional classifier by 20 pp. Principal Component Analysis (PCA) showed negligible benefit for classical methods, whereas transfer learning substantially improved performance under limited-data conditions. Finally, we outline how these models integrate into a real-time Data-Driven Decision Support System for automated waste sorting, highlighting potential reductions in landfill use and lifecycle environmental impacts.)
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