# Benchmarking Deep Learning-Based Object Detection Models on Feature Deficient Astrophotography Imagery Dataset

Source: [Benchmarking Deep Learning-Based Object Detection Models on Feature Deficient Astrophotography Imagery Dataset](<https://www.sota2.com/research/paper/68fa4b491e209131ce944bc6>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-11-20T05:01:37.411Z

Authors: Shantanusinh Parmar

Published: 2025-08-04T10:03:40.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2508.06537>)

[PDF](<https://arxiv.org/pdf/2508.06537.pdf>)