SAMSort: vision foundation model for sorting construction and demolition waste

The rapid growth of construction and demolition waste (CDW) poses significant challenges to environmental sustainability and the availability of land resources, particularly in densely populated cities like Hong Kong, highlighting the urgent need for efficient waste sorting and recycling techniques. Traditional CDW sorting facilities (CWSFs) rely heavily on manual and mechanical operations, which are labour-intensive, costly, and often inefficient. Recent advances in computer vision offers new opportunities for automating CDW sorting, yet most existing models require large training datasets, substantial computational resources, and generally cannot estimate the precise area proportion of each waste category, limiting their practical deployment. To address these challenges, this paper introduces SAMSort, a novel framework that adapts the Segment Anything Model (SAM) for CDW sorting through parameter-efficient fine-tuning (PEFT). Six types of PEFT layers, including transformer adapter, attention adapter, multilayer perceptron adapter, and three variants of low-rank adaptation (LoRA Types I–III), are adopted to reduce the number of trainable parameters. In addition, the performance of all 24 possible combinations is evaluated. A new dataset collected from Tseung Kwan O (TKO) CWSF is constructed for model training and evaluation. The results show that SAMSort achieves competitive waste sorting performance, with an F1-score of 0.764 and an Intersection of Union of 0.670 using only 1 % of the parameters required for full fine-tuning.