在稀疏星空图像上测试深度学习目标检测模型表现

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

• 用手机拍摄的星空图像数据集评估检测模型
• 模型在信号稀疏条件下准确率显著下降
• 适合天文学图像分析与低资源场景研究者

目标检测模型通常在ImageNet、COCO和PASCAL VOC等日常物体数据集上训练，但这些数据集缺乏非商业领域常见的信号稀疏性。MobilTelesco是一个基于智能手机的天文摄影数据集，提供了稀疏的夜空图像，弥补了这一空白。本文在此数据集上对多个检测模型进行基准测试，揭示了在特征不足条件下的性能挑战。

原文摘要 · Abstract (English)

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.

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