YOLO-SAATD: An efficient SAR airport and aircraft target detector
While object detection performs well in natural images, it faces challenges in Synthetic Aperture Radar (SAR) images for detecting airports and aircraft due to discrete scattering points, complex backgrounds, and multi-scale targets. Existing methods struggle with computational inefficiency, omissio...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-06-01
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| Series: | Visual Informatics |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2468502X25000233 |
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| Summary: | While object detection performs well in natural images, it faces challenges in Synthetic Aperture Radar (SAR) images for detecting airports and aircraft due to discrete scattering points, complex backgrounds, and multi-scale targets. Existing methods struggle with computational inefficiency, omission of small targets, and low accuracy. We propose a SAR airport and aircraft target detection model based on YOLO, named YOLO-SAATD (You Only Look Once-SAR Airport and Aircraft Target Detector), which tackles the aforementioned challenges from three perspectives: 1. Efficiency: A lightweight hierarchical multi-scale backbone reduces parameters and enhances detection speed. 2. Fine granularity: A ”ScaleNimble Neck” integrates feature reshaping and scale-aware aggregation to enhance detail detection and feature capture in multi-scale SAR images. 3. Precision: Wise-IoU loss function is used to optimize bounding box localization and enhance detection accuracy. Experiments on the SAR-Airport-1.0 and SAR-AirCraft-1.0 datasets show that YOLO-SAATD improves precision and mAP50 by 1%-2%, increases detection frame rate by 15%, and reduces model parameters by 25% compared to YOLOv8n, thus validating its effectiveness for SAR airport and aircraft target detection. |
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| ISSN: | 2468-502X |