Region versus query based instance segmentation models: application to the estimation of aggregated TiO₂ particles size distribution measured by SEM

Abstract Instance segmentation model performances is commonly evaluated through challenges using toy datasets such as CIFAR and ImageNet. Although, it is necessary to help improving state-of-the-art models, it remains far from critical industrial application where accurate measurement is required to ensure optimal process control, quality assurance and safety. This paper compares the performance of ready-to-deploy state-of-the-art instance segmentation models, specifically region and query-based models, across COCO and task-specific designed metrics in the context of the estimation of aggregated TiO 2 particle size distribution from scanning electron microscopy (SEM) measurements. The numerical comparison is conducted on custom datasets, addressing well-known challenges of SEM microscopists: high resolution images showing a massive number of overlapping instances, images measured with diverse imaging parameters (operator control) or the presence of a mixture of aggregated particles. Our findings highlight the strengths and limitations of each model category, providing insights into their suitability for high-stakes industrial deployment. Finally, we propose a customized inference procedure that leverages the performance of transformers on images that contain a small number of instances within dense and clustered scenes.