ParticleSAM: Small Particle Segmentation for Material Quality Monitoring in Recycling Processes
This addresses the need for automated, efficient quality control in the construction industry, but it is incremental as it adapts an existing model to a specific domain.
The paper tackles the problem of quality monitoring in recycled construction materials by proposing ParticleSAM, an adaptation of a segmentation foundation model for images with hundreds of small and dense particles, and validates its advantages over the original method in experiments.
The construction industry represents a major sector in terms of resource consumption. Recycled construction material has high reuse potential, but quality monitoring of the aggregates is typically still performed with manual methods. Vision-based machine learning methods could offer a faster and more efficient solution to this problem, but existing segmentation methods are by design not directly applicable to images with hundreds of small particles. In this paper, we propose ParticleSAM, an adaptation of the segmentation foundation model to images with small and dense objects such as the ones often encountered in construction material particles. Moreover, we create a new dense multi-particle dataset simulated from isolated particle images with the assistance of an automated data generation and labeling pipeline. This dataset serves as a benchmark for visual material quality control automation while our segmentation approach has the potential to be valuable in application areas beyond construction where small-particle segmentation is needed. Our experimental results validate the advantages of our method by comparing to the original SAM method both in quantitative and qualitative experiments.