{"ID":2886834,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.06537","arxiv_id":"2508.06537","title":"Benchmarking Deep Learning-Based Object Detection Models on Feature Deficient Astrophotography Imagery Dataset","abstract":"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.","short_abstract":"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 benchmar...","url_abs":"https://arxiv.org/abs/2508.06537","url_pdf":"https://arxiv.org/pdf/2508.06537v2","authors":"[\"Shantanusinh Parmar\"]","published":"2025-08-04T10:03:40Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"astro-ph.IM\"]","methods":"[]","has_code":false}