Comparative Analysis of YOLO-Based Algorithms for Vehicle Detection in Aerial Imagery
Real-time object detection has become an essential tool in applications such as traffic surveillance, autonomous vehicles, and industrial monitoring. Among various algorithms, the You Only Look Once (YOLO) series has garnered significant attention for its balance between speed and accuracy. Since it...
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2025-07-01
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| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://isprs-archives.copernicus.org/articles/XLVIII-G-2025/411/2025/isprs-archives-XLVIII-G-2025-411-2025.pdf |
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| author | A. Dustali M. Hasanlou S. M. Azimi |
| author_facet | A. Dustali M. Hasanlou S. M. Azimi |
| author_sort | A. Dustali |
| collection | DOAJ |
| description | Real-time object detection has become an essential tool in applications such as traffic surveillance, autonomous vehicles, and industrial monitoring. Among various algorithms, the You Only Look Once (YOLO) series has garnered significant attention for its balance between speed and accuracy. Since its introduction in 2016, YOLO has seen significant advancements and it has been widely adopted due to its ability to provide fast and accurate real-time detection. Over the years, different versions, including YOLO-v1 to YOLO-v11, have introduced improvements in both accuracy and speed. This paper presents a comparative analysis of four recent versions of YOLO-v8-n, YOLO-v9-t, YOLO-v10-n, and YOLO-v11-n focusing on evaluating their detection accuracy and speed in aerial imagery using the EAGLE dataset. Each version incorporates specific advancements aimed at improving performance under different conditions. The study examines the models using a standardized dataset of aerial images with varying illumination and weather conditions. Key performance metrics, such as inference time and Average Precision (AP), are used to evaluate how each model performs in the vehicle detection task in challenging environments. The results provide valuable insights into the suitability of these YOLO models for real-world applications, particularly in dynamic urban environments and areas where traditional camera systems may be less effective. This study aims to identify the fastest and most accurate YOLO model for vehicle detection in aerial imagery using embedded GPU board of Nvidia Jetson AGX Xavier, contributing to the performance enhancement in real-time surveillance and monitoring systems. |
| format | Article |
| id | doaj-art-2e06ba9498ef404f978577e273d4a322 |
| institution | DOAJ |
| issn | 1682-1750 2194-9034 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj-art-2e06ba9498ef404f978577e273d4a3222025-08-20T02:45:28ZengCopernicus PublicationsThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences1682-17502194-90342025-07-01XLVIII-G-202541141610.5194/isprs-archives-XLVIII-G-2025-411-2025Comparative Analysis of YOLO-Based Algorithms for Vehicle Detection in Aerial ImageryA. Dustali0M. Hasanlou1S. M. Azimi2School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, IranSchool of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, IranRemote Sensing Technology Institute, German Aerospace Center (DLR), Oberpfaffenhofen, GermanyReal-time object detection has become an essential tool in applications such as traffic surveillance, autonomous vehicles, and industrial monitoring. Among various algorithms, the You Only Look Once (YOLO) series has garnered significant attention for its balance between speed and accuracy. Since its introduction in 2016, YOLO has seen significant advancements and it has been widely adopted due to its ability to provide fast and accurate real-time detection. Over the years, different versions, including YOLO-v1 to YOLO-v11, have introduced improvements in both accuracy and speed. This paper presents a comparative analysis of four recent versions of YOLO-v8-n, YOLO-v9-t, YOLO-v10-n, and YOLO-v11-n focusing on evaluating their detection accuracy and speed in aerial imagery using the EAGLE dataset. Each version incorporates specific advancements aimed at improving performance under different conditions. The study examines the models using a standardized dataset of aerial images with varying illumination and weather conditions. Key performance metrics, such as inference time and Average Precision (AP), are used to evaluate how each model performs in the vehicle detection task in challenging environments. The results provide valuable insights into the suitability of these YOLO models for real-world applications, particularly in dynamic urban environments and areas where traditional camera systems may be less effective. This study aims to identify the fastest and most accurate YOLO model for vehicle detection in aerial imagery using embedded GPU board of Nvidia Jetson AGX Xavier, contributing to the performance enhancement in real-time surveillance and monitoring systems.https://isprs-archives.copernicus.org/articles/XLVIII-G-2025/411/2025/isprs-archives-XLVIII-G-2025-411-2025.pdf |
| spellingShingle | A. Dustali M. Hasanlou S. M. Azimi Comparative Analysis of YOLO-Based Algorithms for Vehicle Detection in Aerial Imagery The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | Comparative Analysis of YOLO-Based Algorithms for Vehicle Detection in Aerial Imagery |
| title_full | Comparative Analysis of YOLO-Based Algorithms for Vehicle Detection in Aerial Imagery |
| title_fullStr | Comparative Analysis of YOLO-Based Algorithms for Vehicle Detection in Aerial Imagery |
| title_full_unstemmed | Comparative Analysis of YOLO-Based Algorithms for Vehicle Detection in Aerial Imagery |
| title_short | Comparative Analysis of YOLO-Based Algorithms for Vehicle Detection in Aerial Imagery |
| title_sort | comparative analysis of yolo based algorithms for vehicle detection in aerial imagery |
| url | https://isprs-archives.copernicus.org/articles/XLVIII-G-2025/411/2025/isprs-archives-XLVIII-G-2025-411-2025.pdf |
| work_keys_str_mv | AT adustali comparativeanalysisofyolobasedalgorithmsforvehicledetectioninaerialimagery AT mhasanlou comparativeanalysisofyolobasedalgorithmsforvehicledetectioninaerialimagery AT smazimi comparativeanalysisofyolobasedalgorithmsforvehicledetectioninaerialimagery |