A synthetic segmentation dataset generator using a 3D modeling framework and raycaster: a mining industry application
Many industries utilize deep learning methods to increase efficiency and reduce costs. One of these methods, image segmentation, is used for object detection and recognition in localization and mapping. Segmentation models are trained using labeled datasets; however, manually creating datasets for e...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-12-01
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| Series: | Frontiers in Artificial Intelligence |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/frai.2024.1453931/full |
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| author | Wilhelm Johannes Kilian Jaco Prinsloo Jan Vosloo Stéphan Taljaard |
| author_facet | Wilhelm Johannes Kilian Jaco Prinsloo Jan Vosloo Stéphan Taljaard |
| author_sort | Wilhelm Johannes Kilian |
| collection | DOAJ |
| description | Many industries utilize deep learning methods to increase efficiency and reduce costs. One of these methods, image segmentation, is used for object detection and recognition in localization and mapping. Segmentation models are trained using labeled datasets; however, manually creating datasets for every application, including deep-level mining, is time-consuming and typically expensive. Recently, many papers have shown that using synthetic datasets (digital recreations of real-world scenes) for training produces highly-accurate segmentation models. This paper proposes a synthetic segmentation dataset generator using a 3D modeling framework and raycaster. The generator was applied to a deep-level mining case study and produced a dataset containing labeled images of scenes typically found in this environment, therefore removing the requirement to create the dataset manually. Validation showed high accuracy segmentation after model training using the generated dataset (compared to other applications that use real-world datasets). Furthermore, the generator can be customized to produce datasets for many other applications. |
| format | Article |
| id | doaj-art-7dcf8ccb1ea448108c8790904d3a7e41 |
| institution | DOAJ |
| issn | 2624-8212 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Artificial Intelligence |
| spelling | doaj-art-7dcf8ccb1ea448108c8790904d3a7e412025-08-20T02:50:14ZengFrontiers Media S.A.Frontiers in Artificial Intelligence2624-82122024-12-01710.3389/frai.2024.14539311453931A synthetic segmentation dataset generator using a 3D modeling framework and raycaster: a mining industry applicationWilhelm Johannes KilianJaco PrinslooJan VoslooStéphan TaljaardMany industries utilize deep learning methods to increase efficiency and reduce costs. One of these methods, image segmentation, is used for object detection and recognition in localization and mapping. Segmentation models are trained using labeled datasets; however, manually creating datasets for every application, including deep-level mining, is time-consuming and typically expensive. Recently, many papers have shown that using synthetic datasets (digital recreations of real-world scenes) for training produces highly-accurate segmentation models. This paper proposes a synthetic segmentation dataset generator using a 3D modeling framework and raycaster. The generator was applied to a deep-level mining case study and produced a dataset containing labeled images of scenes typically found in this environment, therefore removing the requirement to create the dataset manually. Validation showed high accuracy segmentation after model training using the generated dataset (compared to other applications that use real-world datasets). Furthermore, the generator can be customized to produce datasets for many other applications.https://www.frontiersin.org/articles/10.3389/frai.2024.1453931/fulldeep learningcomputer visionimage segmentationdeep-level miningreal applications in engineering |
| spellingShingle | Wilhelm Johannes Kilian Jaco Prinsloo Jan Vosloo Stéphan Taljaard A synthetic segmentation dataset generator using a 3D modeling framework and raycaster: a mining industry application Frontiers in Artificial Intelligence deep learning computer vision image segmentation deep-level mining real applications in engineering |
| title | A synthetic segmentation dataset generator using a 3D modeling framework and raycaster: a mining industry application |
| title_full | A synthetic segmentation dataset generator using a 3D modeling framework and raycaster: a mining industry application |
| title_fullStr | A synthetic segmentation dataset generator using a 3D modeling framework and raycaster: a mining industry application |
| title_full_unstemmed | A synthetic segmentation dataset generator using a 3D modeling framework and raycaster: a mining industry application |
| title_short | A synthetic segmentation dataset generator using a 3D modeling framework and raycaster: a mining industry application |
| title_sort | synthetic segmentation dataset generator using a 3d modeling framework and raycaster a mining industry application |
| topic | deep learning computer vision image segmentation deep-level mining real applications in engineering |
| url | https://www.frontiersin.org/articles/10.3389/frai.2024.1453931/full |
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