用深度特征+传统模型实现建筑垃圾高精度分类，准确率超99%。
Hybrid Deep Feature Extraction and ML for Construction and Demolition Debris Classification
- 结合预训练Xception网络提取特征，再用SVM等经典模型分类。
- 在4类垃圾上达到99.5%准确率和宏平均F1值，优于复杂深度模型。
- 适合部署于工地现场，可为机器人自动化分拣提供支持。
建筑行业产生大量废弃物，高效分类对可持续废物管理与资源回收至关重要。本研究提出一种融合深度特征提取与经典机器学习分类器的视觉识别流程，用于自动分类建筑与拆除（C&D）垃圾。研究从阿联酋真实施工现场采集了1,800张平衡且高质量的图像，涵盖陶瓷/瓷砖、混凝土、垃圾/废物、木材四类材料，覆盖多样实际工况。采用预训练Xception网络提取深层特征，并系统评估了SVM、kNN、Bagged Trees、LDA及逻辑回归等多种机器学习分类器。结果表明，基于Xception特征与线性SVM、kNN或Bagged Trees组成的混合模型达到当前最优性能，准确率高达99.5%，宏平均F1得分也领先于更复杂的端到端深度学习方法。该方法具备良好的鲁棒性，适用于现场部署，为未来与机器人及现场自动化系统集成提供了可行路径。
原文摘要 · Abstract (English)
The construction industry produces significant volumes of debris, making effective sorting and classification critical for sustainable waste management and resource recovery. This study presents a hybrid vision-based pipeline that integrates deep feature extraction with classical machine learning (ML) classifiers for automated construction and demolition (C\&D) debris classification. A novel dataset comprising 1,800 balanced, high-quality images representing four material categories, Ceramic/Tile, Concrete, Trash/Waste, and Wood was collected from real construction sites in the UAE, capturing diverse real-world conditions. Deep features were extracted using a pre-trained Xception network, and multiple ML classifiers, including SVM, kNN, Bagged Trees, LDA, and Logistic Regression, were systematically evaluated. The results demonstrate that hybrid pipelines using Xception features with simple classifiers such as Linear SVM, kNN, and Bagged Trees achieve state-of-the-art performance, with up to 99.5\% accuracy and macro-F1 scores, surpassing more complex or end-to-end deep learning approaches. The analysis highlights the operational benefits of this approach for robust, field-deployable debris identification and provides pathways for future integration with robotics and onsite automation systems.
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