Department of Real Estate and Construction, The University of Hong Kong, Pokfulam Road, Hong Kong, China.
J Environ Manage. 2022 Mar 1;305:114405. doi: 10.1016/j.jenvman.2021.114405. Epub 2022 Jan 4.
Recognition of construction waste compositions using computer vision (CV) is increasingly explored to enable its subsequent management, e.g., determining chargeable levy at disposal facilities or waste sorting using robot arms. However, the applicability of existing CV-enabled construction waste recognition in real-life scenarios is limited by their relatively low accuracy, characterized by a failure to distinguish boundaries among different waste materials. This paper aims to propose a novel boundary-aware Transformer (BAT) model for fine-grained composition recognition of construction waste mixtures. First, a pre-processing workflow is devised to separate the hard-to-recognize edges from the background. Second, a Transformer structure with a self-designed cascade decoder is developed to segment different waste materials from construction waste mixtures. Finally, a learning-enabled edge refinement scheme is used to fine-tune the ignored boundaries, further boosting the segmentation precision. The performance of the BAT model was evaluated on a benchmark dataset comprising nine types of materials in a cluttered and mixture state. It recorded a 5.48% improvement of MIoU (mean intersection over union) and 3.65% of MAcc (Mean Accuracy) against the baseline. The research contributes to the body of interdisciplinary knowledge by presenting a novel deep learning model for construction waste material semantic segmentation. It can also expedite the applications of CV in construction waste management to achieve a circular economy.
利用计算机视觉（CV）识别建筑废弃物的组成部分正越来越多地被探索，以实现其后续管理，例如，在处置设施中确定应缴纳的费用，或使用机械臂进行废物分类。然而，现有的基于 CV 的建筑废弃物识别方法在实际场景中的适用性受到限制，原因是它们的准确性相对较低，无法准确区分不同废弃物之间的边界。本文旨在提出一种新颖的边界感知 Transformer（BAT）模型，用于精细化识别建筑废弃物混合物的组成部分。首先，设计了一个预处理工作流程，将难以识别的边缘与背景分离。其次，开发了一种带有自设计级联解码器的 Transformer 结构，用于从建筑废弃物混合物中分割不同的废弃物。最后，采用学习驱动的边缘细化方案，对被忽略的边界进行微调，进一步提高分割精度。在一个包含九种混杂和混合状态材料的基准数据集上对 BAT 模型进行了评估。与基线相比，MIoU（平均交并比）提高了 5.48%，MAcc（平均准确率）提高了 3.65%。本研究通过提出一种新的建筑废弃物材料语义分割深度学习模型，为跨学科知识体系做出了贡献。它还可以加快 CV 在建筑废弃物管理中的应用，以实现循环经济。
