Region versus query based instance segmentation models: application to the estimation of aggregated TiO2 particles size distribution measured by SEM
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 o...
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| Format: | Article |
| Language: | English |
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IOP Publishing
2025-01-01
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| Series: | Machine Learning: Science and Technology |
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| Online Access: | https://doi.org/10.1088/2632-2153/add239 |
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| author | Paul Monchot Loïc Coquelin Nicolas Fischer |
| author_facet | Paul Monchot Loïc Coquelin Nicolas Fischer |
| author_sort | Paul Monchot |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-8b060ff0628147369f3e5fbdc1b9e6bc |
| institution | Kabale University |
| issn | 2632-2153 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Machine Learning: Science and Technology |
| spelling | doaj-art-8b060ff0628147369f3e5fbdc1b9e6bc2025-08-20T03:47:32ZengIOP PublishingMachine Learning: Science and Technology2632-21532025-01-016202050210.1088/2632-2153/add239Region versus query based instance segmentation models: application to the estimation of aggregated TiO2 particles size distribution measured by SEMPaul Monchot0https://orcid.org/0000-0002-6658-1798Loïc Coquelin1https://orcid.org/0000-0001-5834-252XNicolas Fischer2https://orcid.org/0000-0003-3628-8117Data Science and Uncertainty Department, National Laboratory of Metrology and Testing , 29 avenue Roger Hennequin, 78197 Trappes, FranceData Science and Uncertainty Department, National Laboratory of Metrology and Testing , 29 avenue Roger Hennequin, 78197 Trappes, FranceData Science and Uncertainty Department, National Laboratory of Metrology and Testing , 29 avenue Roger Hennequin, 78197 Trappes, FranceInstance 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.https://doi.org/10.1088/2632-2153/add239scanning electron microscopybenchmarktransformersdeep learningparticle size distributioninstance segmentation |
| spellingShingle | Paul Monchot Loïc Coquelin Nicolas Fischer Region versus query based instance segmentation models: application to the estimation of aggregated TiO2 particles size distribution measured by SEM Machine Learning: Science and Technology scanning electron microscopy benchmark transformers deep learning particle size distribution instance segmentation |
| title | Region versus query based instance segmentation models: application to the estimation of aggregated TiO2 particles size distribution measured by SEM |
| title_full | Region versus query based instance segmentation models: application to the estimation of aggregated TiO2 particles size distribution measured by SEM |
| title_fullStr | Region versus query based instance segmentation models: application to the estimation of aggregated TiO2 particles size distribution measured by SEM |
| title_full_unstemmed | Region versus query based instance segmentation models: application to the estimation of aggregated TiO2 particles size distribution measured by SEM |
| title_short | Region versus query based instance segmentation models: application to the estimation of aggregated TiO2 particles size distribution measured by SEM |
| title_sort | region versus query based instance segmentation models application to the estimation of aggregated tio2 particles size distribution measured by sem |
| topic | scanning electron microscopy benchmark transformers deep learning particle size distribution instance segmentation |
| url | https://doi.org/10.1088/2632-2153/add239 |
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