RealWaste

The accurate classification of landfill waste diversion plays a critical role in efficient waste management practices. Traditional approaches, such as visual inspection, weighing and volume measurement, and manual sorting, have been widely used but suffer from subjectivity, scalability, and labour requirements. In contrast, machine learning approaches, particularly Convolutional Neural Networks (CNN), have emerged as powerful deep learning models for waste detection and classification. This paper analyses VGG-16, InceptionResNetV2, DenseNet121, Inception V3, and MobileNetV2 models to classify real-life waste when trained on pristine and unadulterated materials, versus samples collected at a landfill site. When training on DiversionNet, the unadulterated material dataset with labels required for landfill modelling, classification accuracy was limited to 49.69% in the real environment. Using real-world samples in the newly formed RealWaste dataset showed that practical applications for deep learning in waste classification are possible, with Inception V3 reaching 89.19% classification accuracy on the full spectrum of labels required for accurate modelling.

垃圾分流精准分类对于高效垃圾管理实践具有至关重要的作用。传统方法如目视检查、称重与体积测量、人工分拣虽已广泛应用，但存在主观性强、可扩展性受限、需大量人力投入的缺陷。相比之下，机器学习方法，尤其是卷积神经网络（Convolutional Neural Networks, CNN），已成为垃圾检测与分类领域极具竞争力的深度学习模型。本文针对VGG-16、InceptionResNetV2、DenseNet121、Inception V3以及MobileNetV2模型展开分析，分别以纯净未掺杂的物料与垃圾填埋场采集的样本作为训练集，开展真实场景下的垃圾分类任务。当使用用于垃圾填埋建模所需标签的纯净物料数据集DiversionNet进行训练时，真实场景下的分类准确率仅为49.69%。而在新构建的RealWaste真实样本数据集上开展训练的结果表明，深度学习在垃圾分类领域的实际应用具备可行性，其中Inception V3模型在精准建模所需的全类别标签上实现了89.19%的分类准确率。

搜集汇总

背景与挑战

背景概述

RealWaste是一个包含4752张图像的数据集，用于垃圾填埋场废物分类，涵盖9个类别，旨在通过深度学习模型提高分类准确性。