Waste classification models have become highly accurate at sorting waste, often exceeding 95% on benchmark datasets. However, these models fail to account for contamination in recyclable waste. We present EcoBin, a two-stage deep convolutional neural network that classifies household waste by its disposal pathway and that explicitly accounts for contamination. The first stage is a base waste classifier built on an EfficientNetV2-S backbone that assigns each of the thirty waste categories in our dataset to one of four disposal pathways. The second stage is a contamination classifier that inspects any item routed toward recycling and overrides the decision to garbage when contamination is detected. Because no public dataset of contaminated recyclables exists, we synthesize one by segmenting images of clean recyclable objects with a U2-Net model and compositing realistic contamination textures onto their surfaces. The first stage achieves 87.42% test accuracy and a 96.13% pathway-adjusted accuracy. Meanwhile, the contamination stage distinguishes clean from contaminated items with a 0.99 ROC-AUC. On a test set of contaminated recyclables, the complete pipeline routes 24 of 25 items correctly, compared with only 1 of 25 for the base classifier alone. A McNemar's test confirms that the improvement contributed by the contamination stage is statistically significant (p < 0.001).

翻译：摘要：现有垃圾分类模型在基准数据集上分类准确率常超过95%，但未能有效处理可回收垃圾中的污染问题。本文提出EcoBin双阶段深度卷积神经网络，该模型通过显式引入污染识别机制，对家庭垃圾的处理路径进行分类。第一阶段构建基于EfficientNetV2-S骨干网络的基准分类器，将数据集中的三十类垃圾归入四种处理路径；第二阶段为污染检测分类器，专门检测被判定为可回收的物品，当检测到污染时将其重新判定为垃圾。鉴于目前缺乏公开的污染可回收物数据集，我们通过U2-Net模型对清洁可回收物品图像进行分割，并在其表面合成真实污染纹理来构建合成数据集。第一阶段达到87.42%的测试准确率与96.13%的路径调整准确率，污染检测阶段对清洁与污染物品的区分ROC-AUC达到0.99。在污染可回收物测试集上，完整管道正确分类25件物品中的24件，而单独使用基准分类器仅正确分类25件中的1件。McNemar检验证实污染检测阶段带来的改进具有统计学显著性（p < 0.001）。