{"title":"探索你只看一次v8和v9在非常高分辨率的遥感图像中有效的飞机检测","authors":"Doğu İlmak , Tolga Bakirman , Elif Sertel","doi":"10.1016/j.engappai.2025.111854","DOIUrl":null,"url":null,"abstract":"<div><div>Automatic airplane detection from satellite images using deep learning methods produces valuable geospatial information for a wide range of applications, including aviation safety, defence, airport and disaster management. You Only Look Once (YOLO) models have been widely used for various geospatial tasks; however, their application to airplane detection in very high-resolution (VHR) remote sensing imagery, particularly YOLOv8 and YOLOv9, remains underexplored. This study aims to assess the performance of YOLOv8 and YOLOv9 architectures in the context of airplane detection using High Resolution Planes (HRPlanes) dataset. First, we examine the impact of various hyperparameters on the performance of YOLOv8 models to propose optimal hyperparameter and model variant combinations. Second, we compare the best-performing YOLOv8 configurations with their YOLOv9 counterparts to evaluate potential improvements. Third, we assess the generalizability and transferability of the top-performing models by testing them across independent airplane detection datasets. Lastly, we perform an operational assessment of inference performance by analyzing trade-offs between network size, input image resolution and processing time. The optimal performance was achieved with the YOLOv8x model using 960x960 network size and data augmentation, resulting in 98.99 % F1-Score, 99.12 % Precision, 98.86 % Recall, 99.35 % Mean Average Precision (mAP)50, and 89.82 % mAP50-95. YOLOv9e achieved comparable performance with fewer parameters (57.3 vs. 68.2 million) and lower computational cost (189.0 vs. 257.8 giga floating point operations per second (GFLOPS)), offering up to a 27 % reduction in computational cost. These findings highlight the practical potential of both YOLOv8 and YOLOv9 for high-precision airplane detection in VHR remote sensing imagery. The HRPlanes dataset and model weights are publicly available at: <span><span>https://github.com/RSandAI/Efficient-YOLO-RS-Airplane-Detection</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"160 ","pages":"Article 111854"},"PeriodicalIF":8.0000,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Exploring You Only Look Once v8 and v9 for efficient airplane detection in very high resolution remote sensing imagery\",\"authors\":\"Doğu İlmak , Tolga Bakirman , Elif Sertel\",\"doi\":\"10.1016/j.engappai.2025.111854\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Automatic airplane detection from satellite images using deep learning methods produces valuable geospatial information for a wide range of applications, including aviation safety, defence, airport and disaster management. You Only Look Once (YOLO) models have been widely used for various geospatial tasks; however, their application to airplane detection in very high-resolution (VHR) remote sensing imagery, particularly YOLOv8 and YOLOv9, remains underexplored. This study aims to assess the performance of YOLOv8 and YOLOv9 architectures in the context of airplane detection using High Resolution Planes (HRPlanes) dataset. First, we examine the impact of various hyperparameters on the performance of YOLOv8 models to propose optimal hyperparameter and model variant combinations. Second, we compare the best-performing YOLOv8 configurations with their YOLOv9 counterparts to evaluate potential improvements. Third, we assess the generalizability and transferability of the top-performing models by testing them across independent airplane detection datasets. Lastly, we perform an operational assessment of inference performance by analyzing trade-offs between network size, input image resolution and processing time. The optimal performance was achieved with the YOLOv8x model using 960x960 network size and data augmentation, resulting in 98.99 % F1-Score, 99.12 % Precision, 98.86 % Recall, 99.35 % Mean Average Precision (mAP)50, and 89.82 % mAP50-95. YOLOv9e achieved comparable performance with fewer parameters (57.3 vs. 68.2 million) and lower computational cost (189.0 vs. 257.8 giga floating point operations per second (GFLOPS)), offering up to a 27 % reduction in computational cost. These findings highlight the practical potential of both YOLOv8 and YOLOv9 for high-precision airplane detection in VHR remote sensing imagery. 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引用次数: 0
摘要
利用深度学习方法从卫星图像中自动检测飞机,可为航空安全、国防、机场和灾害管理等广泛应用提供有价值的地理空间信息。You Only Look Once (YOLO)模型已广泛用于各种地理空间任务;然而,它们在高分辨率(VHR)遥感图像中的飞机探测应用,特别是YOLOv8和YOLOv9,仍未得到充分探索。本研究旨在利用高分辨率飞机(HRPlanes)数据集评估YOLOv8和YOLOv9架构在飞机检测背景下的性能。首先,我们研究了各种超参数对YOLOv8模型性能的影响,提出了最优的超参数和模型变体组合。其次,我们将性能最好的YOLOv8配置与其对应的YOLOv9配置进行比较,以评估潜在的改进。第三,我们通过在独立的飞机检测数据集上测试表现最好的模型来评估它们的泛化性和可移植性。最后,我们通过分析网络大小、输入图像分辨率和处理时间之间的权衡,对推理性能进行了操作评估。使用960x960网络大小和数据扩充的YOLOv8x模型获得了最佳性能,F1-Score为98.99%,Precision为99.12%,Recall为98.86%,Mean Average Precision (mAP)50为99.35%,mAP50-95为89.82%。YOLOv9e以更少的参数(57.3 vs. 6820万)和更低的计算成本(189.0 vs. 257.8千兆浮点运算/秒(GFLOPS))实现了相当的性能,计算成本降低了27%。这些发现突出了YOLOv8和YOLOv9在VHR遥感图像中高精度飞机探测的实际潜力。HRPlanes数据集和模型权重可在:https://github.com/RSandAI/Efficient-YOLO-RS-Airplane-Detection公开获取。
Exploring You Only Look Once v8 and v9 for efficient airplane detection in very high resolution remote sensing imagery
Automatic airplane detection from satellite images using deep learning methods produces valuable geospatial information for a wide range of applications, including aviation safety, defence, airport and disaster management. You Only Look Once (YOLO) models have been widely used for various geospatial tasks; however, their application to airplane detection in very high-resolution (VHR) remote sensing imagery, particularly YOLOv8 and YOLOv9, remains underexplored. This study aims to assess the performance of YOLOv8 and YOLOv9 architectures in the context of airplane detection using High Resolution Planes (HRPlanes) dataset. First, we examine the impact of various hyperparameters on the performance of YOLOv8 models to propose optimal hyperparameter and model variant combinations. Second, we compare the best-performing YOLOv8 configurations with their YOLOv9 counterparts to evaluate potential improvements. Third, we assess the generalizability and transferability of the top-performing models by testing them across independent airplane detection datasets. Lastly, we perform an operational assessment of inference performance by analyzing trade-offs between network size, input image resolution and processing time. The optimal performance was achieved with the YOLOv8x model using 960x960 network size and data augmentation, resulting in 98.99 % F1-Score, 99.12 % Precision, 98.86 % Recall, 99.35 % Mean Average Precision (mAP)50, and 89.82 % mAP50-95. YOLOv9e achieved comparable performance with fewer parameters (57.3 vs. 68.2 million) and lower computational cost (189.0 vs. 257.8 giga floating point operations per second (GFLOPS)), offering up to a 27 % reduction in computational cost. These findings highlight the practical potential of both YOLOv8 and YOLOv9 for high-precision airplane detection in VHR remote sensing imagery. The HRPlanes dataset and model weights are publicly available at: https://github.com/RSandAI/Efficient-YOLO-RS-Airplane-Detection.
期刊介绍:
Artificial Intelligence (AI) is pivotal in driving the fourth industrial revolution, witnessing remarkable advancements across various machine learning methodologies. AI techniques have become indispensable tools for practicing engineers, enabling them to tackle previously insurmountable challenges. Engineering Applications of Artificial Intelligence serves as a global platform for the swift dissemination of research elucidating the practical application of AI methods across all engineering disciplines. Submitted papers are expected to present novel aspects of AI utilized in real-world engineering applications, validated using publicly available datasets to ensure the replicability of research outcomes. Join us in exploring the transformative potential of AI in engineering.