Benchmarking Deep Learning-Based Object Detection Models on Feature Deficient Astrophotography Imagery Dataset
This work addresses object detection for astrophotography applications, but it is incremental as it applies existing methods to a new dataset.
The paper tackled the problem of object detection in feature-deficient astrophotography imagery by benchmarking deep learning models on the MobilTelesco dataset, revealing challenges in sparse night-sky images.
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.