Abstract
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.
Published Version
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