用增强+YOLOv11提升卫星图像中飞机检测精度
Aircraft Detection in Satellite Imagery using Deep Learning Object Detectors
- 先用Gabor滤波降噪提特征，再归一化数据
- 在Satellite Aircraft Dataset上达95% mAP、97%精确率
- 适合实时监控、空管和遥感分析场景
由于广泛的实际应用及固有的挑战（如噪声、图像质量波动、复杂背景），卫星图像中的目标检测受到广泛关注。本文提出一种结合图像增强与深度学习的检测框架，以提高准确性。首先利用Gabor滤波器处理输入图像，提取关键特征并抑制噪声；随后进行归一化，确保数据分布均匀，使模型有效运行；最后采用基于YOLOv11的模型快速学习并识别目标特征。该方法在卫星图像中的飞机检测任务上达到95%的mAP、97%的精确率、85%的召回率和91%的F1分数，表明其具备优异的检测性能，适用于监视、空中交通监控和遥感分析等实时应用场景。
原文摘要 · Abstract (English)
The object detection in satellite imagery has garnered considerable attention due to its extensive real-world applications and the inherent challenges it presents, including noise, fluctuating image quality, and intricate backgrounds. This paper proposed a framework for object detection that combines image enhancement and Deep Learning (DL) to make detection more accurate. First, a Gabor filter is used to process the input image to bring out important features and reduce noise. Then, normalization is applied to make sure that the data is evenly distributed so that the model works properly. After that, a model based on YOLOv11 is used to quickly learn and find object features. The proposed method achieves a mAP of 95%, precision of 97%, recall of 85%, and F1-score of 91%, which demonstrates the superior aircraft detection performance. These results show the framework accurately identify aircraft in satellite imagery and is suitable for real-time applications such as surveillance, air traffic monitoring and remote sensing analysis.
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