Benchmarking computer vision models for automated construction waste sorting

Abstract

Waste sorting is a critical process in construction waste management system. Computer vision (CV) offers waste sorting automation potential by recognizing waste composition and instructing robots or other mechanical devices accordingly. However, how the plethora of CV models developed perform relative to each other remains underexplored, making model selection challenging for researchers and practitioners. This study aims to benchmark existing CV models towards automated construction waste segregation. Seventeen models were selected and trained with unified configuration, and then their performance was evaluated on the aspect of accuracy, efficiency, and robustness, respectively. In experimental results, BEiT attained top accuracy (58.31 % MIoU) while FastFCN had the best efficiency (12.87 ms). SAN displayed the least standard deviation (4.41 %) for robustness evaluation. This research contributes a reliable reference for CV model selection, advancing automated construction waste sorting research and practices, and ultimately promoting efficient recycling while reducing the environmental impact of construction and demolition waste.