COSMICA: A Novel Dataset for Astronomical Object Detection with Evaluation Across Diverse Detection Architectures

Accurate and efficient detection of celestial objects in telescope imagery is a fundamental challenge in both professional and amateur astronomy. Traditional methods often struggle with noise, varying brightness, and object morphology. This paper introduces COSMICA, a novel, curated dataset of manually annotated astronomical images collected from amateur observations. COSMICA enables the development and evaluation of real-time object detection systems intended for practical deployment in observational pipelines. We investigate three modern YOLO architectures, YOLOv8, YOLOv9, and YOLOv11, and two additional object detection models, EfficientDet-Lite0 and MobileNetV3-FasterRCNN-FPN, to assess their performance in detecting comets, galaxies, nebulae, and globular clusters. All models are evaluated using consistent experimental conditions across multiple metrics, including mAP, precision, recall, and inference speed. YOLOv11 demonstrated the highest overall accuracy and computational efficiency, making it a promising candidate for real-world astronomical applications. These results support the feasibility of integrating deep learning-based detection systems into observational astronomy workflows and highlight the importance of domain-specific datasets for training robust AI models.