Transforming Aircraft Detection Through LEO Satellite Imagery and YOLOv9 for Improved Aviation Safety

Bakirci, Murat; Bayraktar, Irem

doi:10.1109/dspa60853.2024.10510106

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2024

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Cited by 8 publications

References 22 publications

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YOLO-Fusion and Internet of Things: Advancing object detection in smart transportation

Tang,

Ye,

Zhou

et al. 2024

Alexandria Engineering Journal

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YOLO-Fusion and Internet of Things: Advancing object detection in smart transportation

Tang,

Ye,

Zhou

et al. 2024

Alexandria Engineering Journal

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A new network model for multiple object detection for autonomous vehicle detection in mining environment

Hanif,

Yu,

Bashir

et al. 2024

IET Image Processing

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Considering the challenges of low multi‐object detection accuracy and difficulty in identifying small targets caused by challenging environmental conditions including irregular lighting patterns and ambient noise levels in the mining environment with autonomous electric locomotives. A new network model based on SOD−YOLOv5s−4L has been proposed to detect multi‐objects for autonomous electric locomotives in underground coal mines. Improvements have been applied in YOLOv5s to construct the SOD−YOLOv5s−4L model, by introducing the SIoU loss function to address the mismatch between real and predicted bounding box directions, facilitating the model to learn target position information more efficiently. This research introduces a decoupled head to enhance feature fusion and improve the positioning precision of the network model, enabling rapid capture of multi‐scale target features. Furthermore, the detection capability of the model has been increased by introducing the small target detection layer which is developed by increasing the number of detection layers from three to four. The experimental results on multiple object detection dataset show that the proposed model achieves significant improvement in mean average precision (mAP) of almost 98% for various types of targets and an average precision (AP) of nearly 99% for small targets on the other hand it achieves 5.19% (mAP) and 9.79% (AP) compared to the YOLOv5s model. Furthermore, comparative analysis with other models like YOLOv7 and YOLOv8 shows that the proposed model has superior performance in terms of object detection.

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You Only Look Once Based-C2fGhost Using Efficient SIoU Loss Function for Airplane Detection

Do,

Ha,

Nguyen

et al. 2024

2024 9th International Conference on Frontiers of Signal Processing (ICFSP)

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Transforming Aircraft Detection Through LEO Satellite Imagery and YOLOv9 for Improved Aviation Safety

Cited by 8 publications

References 22 publications

YOLO-Fusion and Internet of Things: Advancing object detection in smart transportation

YOLO-Fusion and Internet of Things: Advancing object detection in smart transportation

A new network model for multiple object detection for autonomous vehicle detection in mining environment

You Only Look Once Based-C2fGhost Using Efficient SIoU Loss Function for Airplane Detection

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