Department of Civil Engineering, Faculty of Engineering, Monash University, Melbourne, Australia.
Department of Electrical and Computer Systems Engineering, Faculty of Engineering, Monash University, Melbourne, Australia.
Waste Manag. 2024 Dec 15;190:149-160. doi: 10.1016/j.wasman.2024.09.018. Epub 2024 Sep 24.
Optimized and automated methods for handling construction and demolition waste (CDW) are crucial for improving the resource recovery process in waste management. Automated waste recognition is a critical step in this process, and it relies on robust image segmentation techniques. Prompt-guided segmentation methods provide promising results for specific user needs in image recognition. However, the current state-of-the-art segmentation methods trained for generic images perform unsatisfactorily on CDW recognition tasks, indicating a domain gap. To address this gap, a user-guided segmentation pipeline is developed in this study that leverages prompts such as bounding boxes, points, and text to segment CDW in cluttered environments. The adopted approach achieves a class-wise performance of around 70 % in several waste categories, surpassing the state-of-the-art algorithms by 9 % on average. This method allows users to create accurate segmentations by drawing a bounding box, clicking, or providing a text prompt, minimizing the time spent on detailed annotations. Integrating this human-machine system as a user-friendly interface into material recovery facilities enhances the monitoring and processing of waste, leading to better resource recovery outcomes in waste management.
优化和自动化的建筑和拆除废物（CDW）处理方法对于改进废物管理中的资源回收过程至关重要。自动废物识别是该过程中的关键步骤，它依赖于强大的图像分割技术。提示引导的分割方法在图像识别的特定用户需求方面提供了有前景的结果。然而，针对通用图像训练的当前最先进的分割方法在 CDW 识别任务上表现不佳，表明存在领域差距。为了解决这个差距，本研究开发了一种用户引导的分割管道，该管道利用边界框、点和文本等提示在杂乱环境中对 CDW 进行分割。所采用的方法在几个废物类别中实现了约 70%的类别性能，平均比最先进的算法高出 9%。这种方法允许用户通过绘制边界框、点击或提供文本提示来创建准确的分割，从而最大程度地减少详细注释所需的时间。将这种人机系统集成到材料回收设施中作为用户友好的界面，可以增强对废物的监测和处理，从而在废物管理中实现更好的资源回收结果。
