Prasad Vineet, Arashpour Mehrdad
Department of Civil Engineering, Monash University, Melbourne, Australia.
J Environ Manage. 2024 Dec;372:123365. doi: 10.1016/j.jenvman.2024.123365. Epub 2024 Nov 24.
Efficient recycling strategies are crucial for mitigating the adverse environmental impacts of escalating construction and demolition waste (CDW). While automated identification via deep-learning is a promising direction, localizing CDW recyclables is uniquely challenging due to significant clutter and compositional complexity. Recognizing that accurate and fast localization is a strong prerequisite for swift robotic action, this study provides a comprehensive assessment of state-of-the-art (s.o.t.a) real-time instance segmentation of recyclables from complex CDW streams. Unlike previous studies that simplify the real-world conditions, this study curates and employs a high-quality CDW instance segmentation dataset tailored to capture often-ignored domain intricacies such as deformation, contamination, intricate class distinctions, scale variations, and high levels of clutter, ensuring practical applicability. The results show that even advanced networks struggle with complexity of real CDW streams due to high clutter, with segmentation accuracy not surpassing 50%. To address this, the study proposes integrating patch-based inferencing techniques, which help the model focus on cluttered regions more effectively, boosting overall performance by a notable 12.9%. Additionally, to enhance the zero-shot capabilities of s.o.t.a prompt-based real-time segmentation for identifying CDW recyclables, a simple yet effective domain-transfer framework is proposed, substantially increasing number of correct mask predictions by 68%. This study serves as a practical reference for applying deep-learning tools in automated environmental management tasks such as waste sorting and suggests the best architectural framework for practical use.
高效的回收策略对于减轻不断增加的建筑与拆除废物（CDW）对环境的不利影响至关重要。虽然通过深度学习进行自动识别是一个有前景的方向，但由于大量的杂物和成分复杂性，对CDW可回收物进行定位具有独特的挑战性。认识到准确快速的定位是机器人迅速行动的重要前提，本研究对从复杂CDW流中实时分割可回收物的最新技术（s.o.t.a）进行了全面评估。与以往简化现实世界条件的研究不同，本研究精心策划并使用了一个高质量的CDW实例分割数据集，该数据集专门用于捕捉经常被忽视的领域复杂性，如变形、污染、复杂的类别区分、尺度变化和高度杂乱，以确保实际适用性。结果表明，由于杂物过多，即使是先进的网络也难以应对真实CDW流的复杂性，分割准确率不超过50%。为了解决这个问题，该研究提出整合基于补丁的推理技术，这有助于模型更有效地关注杂乱区域，显著提高整体性能12.9%。此外，为了增强用于识别CDW可回收物的基于提示的实时分割的零样本能力，提出了一个简单而有效的域转移框架，将正确掩码预测的数量大幅增加了68%。本研究为在诸如垃圾分类等自动环境管理任务中应用深度学习工具提供了实际参考，并提出了实际使用的最佳架构框架。
