Region versus query based instance segmentation models: application to the estimation of aggregated TiO2 particles size distribution measured by SEM - Paul Monchot; Loïc Coquelin; Nicolas Fischer (Machine Learning: Science and Technology)
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.