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8 June 2024 Comparison of hybrid real and synthetized image sources for training an object detector
Nicolas Hueber, Alexander Pichler
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Proceedings Volume 13035, Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications II; 1303505 (2024) https://doi.org/10.1117/12.3014093
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States
Abstract
Vision-based object detection remains an active research area in both civilian and military domains. While the state-of-the-art relies on deep learning techniques, these demand large multi-context datasets. Given the rarity of open-access datasets for military applications, alternative methods for data collection and training dataset creation are essential. This paper presents a novel vehicle signature acquisition based on indoor 3D-scanning of miniature military vehicles. By using 3D projections of the scanned vehicles as well as off-the-shelves computer aided design models, relevant image signatures are generated showing the vehicle from different perspectives. The resulting context-independent signatures are enhanced with data augmentation techniques and used for object detection model training. The trained models are evaluated by means of aerial test sequences showing real vehicles and situations. Results are compared to state-of-the art methodologies. Our method is shown to be a suitable indoor solution for training a vehicle detector for real situations.
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Nicolas Hueber and Alexander Pichler "Comparison of hybrid real and synthetized image sources for training an object detector", Proc. SPIE 13035, Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications II, 1303505 (8 June 2024); https://doi.org/10.1117/12.3014093
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KEYWORDS
3D modeling
Education and training
Visual process modeling
Solid modeling
3D acquisition
3D image processing
3D scanning
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