Leveraging large-scale vision foundation models for automated construction and demolition waste recognition
Diani Sirimewan, Ahmed Farouk Kineber, Sudharshan N. Raman, Reyes Garcia, Mehrdad Arashpour
Abstract
Effective handling of construction and demolition waste (CDW) is crucial for sustainable resource recovery in industrial settings. This study introduces WasteXtract , a vision-based AI model for class-specific segmentation of CDW designed to automate waste monitoring and sorting at material recovery facilities (MRFs). The theoretical contribution of this research lies in adapting large-scale vision foundation models by incorporating a class-specific mask decoder and a lightweight adapter in a ViT-based image encoder. This approach enables efficient and scalable fine-tuning for the industrial application of CDW recognition, achieving reliable waste detection and classification performance. The engineering applications of WasteXtract are validated across two datasets representing real-world recognition of waste composition in skip-bins and conveyor-belt scenarios for intelligent automation and optimised resource recovery. The segmentation results show that WasteXtract achieves mean weighted intersection over union (WIoU) scores of 0.66 in skip-bin waste composition monitoring and 0.96 in the conveyor-belt scenario. Compared to baseline models such as DeepLabv3+ and U-Net, WasteXtract achieves significantly higher segmentation accuracy while reducing the number of trainable parameters, demonstrating its suitability for deployment in real-world, resource-constrained environments such as MRFs. This study highlights the transformative role of informatics in advancing automation and efficiency within sustainable waste management.