Object detection in remotely sensed satellite pictures is fundamental in many fields such as biophysical, and environmental monitoring. While deep learning algorithms are constantly evolving, they have been mostly implemented and tested on popular ground-based taken photos. This paper critically evaluates and compares a suite of advanced object detection algorithms customized for the task of identifying aircraft within satellite imagery. Using the large HRPlanesV2 dataset, together with a rigorous validation with the GDIT dataset, this research encompasses an array of methodologies including YOLO versions 5 and 8, Faster RCNN, CenterNet, RetinaNet, RTMDet, and DETR, all trained from scratch. This exhaustive training and validation study reveal YOLOv5 as the preeminent model for the specific case of identifying airplanes from remote sensing data, showcasing high precision and adaptability across diverse imaging conditions. This research highlight the nuanced performance landscapes of these algorithms, with YOLOv5 emerging as a robust solution for aerial object detection, underlining its importance through superior mean average precision, Recall, and Intersection over Union scores. The findings described here underscore the fundamental role of algorithm selection aligned with the specific demands of satellite imagery analysis and extend a comprehensive framework to evaluate model efficacy. The benchmark toolkit and codes, available via https://github.com/toelt-llc/FlightScope_Bench, aims to further exploration and innovation in the realm of remote sensing object detection, paving the way for improved analytical methodologies in satellite imagery applications.

翻译：遥感卫星图像中的目标检测是生物物理与环境监测等多个领域的基础。尽管深度学习算法不断发展，但其大多已在流行的地面拍摄照片上实施和测试。本文批判性评估并比较了一系列针对卫星图像中飞机识别任务定制的先进目标检测算法。本研究利用大型HRPlanesV2数据集，结合GDIT数据集的严格验证，涵盖了包括YOLOv5与YOLOv8、Faster RCNN、CenterNet、RetinaNet、RTMDet以及DETR在内的多种方法，所有模型均从头开始训练。这项全面的训练与验证研究表明，在从遥感数据中识别飞机的特定案例中，YOLOv5是最优模型，展现出高精度及对不同成像条件的适应能力。本研究凸显了这些算法性能格局的微妙差异，而YOLOv5作为空中目标检测的稳健解决方案脱颖而出，并通过较高的平均精度均值、召回率及交并比评分强调了其重要性。本文所述发现强调了算法选择需契合卫星图像分析特定需求的基本原则，并提供了一个评估模型效能的全面框架。通过https://github.com/toelt-llc/FlightScope_Bench提供的基准测试工具包和代码，旨在推动遥感目标检测领域的进一步探索与创新，为卫星图像应用中更优分析方法论的发展铺平道路。