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：在遥感卫星图像中的目标检测是生物物理、环境监测等多个领域的基础。虽然深度学习算法不断发展，但大多已在常见的地面拍摄照片上实施和测试。本文严格评估并比较了一套专为卫星图像中飞机识别任务定制的先进目标检测算法。利用大型HRPlanesV2数据集，并结合GDIT数据集的严格验证，本研究涵盖了多种方法，包括YOLOv5和YOLOv8、Faster RCNN、CenterNet、RetinaNet、RTMDet和DETR，这些模型均从零开始训练。这一详尽的训练与验证研究表明，YOLOv5在遥感数据中识别飞机的特定场景下表现卓越，展现出高精度和跨不同成像条件的适应性。本研究凸显了这些算法的细微性能差异，其中YOLOv5成为空中目标检测的稳健解决方案，通过其优越的平均精度均值、召回率和交并比得分，强调了其重要性。本文发现强调了算法选择必须契合卫星图像分析的具体需求，并扩展了一个评估模型效能的综合框架。基准测试工具包和代码可通过https://github.com/toelt-llc/FlightScope_Bench获取，旨在促进遥感目标检测领域的进一步探索和创新，为卫星图像应用中改进分析方法铺平道路。