Benchmarking Deep Learning-Based Object Detection Models on Feature Deficient Astrophotography Imagery Dataset
Fuente:
arXiv
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866909910099296256 |
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| author | Parmar, Shantanusinh |
| author_facet | Parmar, Shantanusinh |
| contents | Object detection models are typically trained on datasets like ImageNet, COCO, and PASCAL VOC, which focus on everyday objects. However, these lack signal sparsity found in non-commercial domains. MobilTelesco, a smartphone-based astrophotography dataset, addresses this by providing sparse night-sky images. We benchmark several detection models on it, highlighting challenges under feature-deficient conditions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_06537 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Benchmarking Deep Learning-Based Object Detection Models on Feature Deficient Astrophotography Imagery Dataset Parmar, Shantanusinh Computer Vision and Pattern Recognition Instrumentation and Methods for Astrophysics Object detection models are typically trained on datasets like ImageNet, COCO, and PASCAL VOC, which focus on everyday objects. However, these lack signal sparsity found in non-commercial domains. MobilTelesco, a smartphone-based astrophotography dataset, addresses this by providing sparse night-sky images. We benchmark several detection models on it, highlighting challenges under feature-deficient conditions. |
| title | Benchmarking Deep Learning-Based Object Detection Models on Feature Deficient Astrophotography Imagery Dataset |
| topic | Computer Vision and Pattern Recognition Instrumentation and Methods for Astrophysics |
| url | https://arxiv.org/abs/2508.06537 |