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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >Local Attention Networks for Occluded Airplane Detection in Remote Sensing Images
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Local Attention Networks for Occluded Airplane Detection in Remote Sensing Images

机译：遥感图像中的遮挡飞机检测的本地注意网络

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摘要

Despite the great progress of deep learning and target detection in recent years, the accurate detection of the occluded targets in remote sensing images still remains a challenge. In this letter, we propose a new detection method called local attention networks to improve the detection of occluded airplanes. Following the idea of "divide and conquer," the proposed method is designed by first dividing an airplane target into four visual parts: head, left/right wings, body, and tail, and then considering the detection as the prediction of the individual key points in each of the visual parts. We further introduce an additional attention branch in the standard detection pipeline to enhance the features and make the model focus on individual parts of a target even if it is only partially visible in the image. Detection results and ablation studies on three remote sensing target detection data sets (including two publicly available ones) demonstrate the effectiveness of our method, especially for occluded airplane targets. In addition, our method outperforms the other state-of-the-art detection methods on these data sets.
机译：尽管近年来深度学习和目标探测的进展良好，但准确地检测遥感图像中的遮挡目标仍然是一个挑战。在这封信中，我们提出了一种新的检测方法，称为本地注意网络，以改善遮挡飞机的检测。在“划分和征服”的思想之后，所提出的方法是通过首先将飞机目标分成四个视觉部件：头部，左/右翼，身体和尾部，然后考虑检测作为单个键的预测每个视觉零件中的点。我们进一步在标准检测管道中引入了额外的关注分支，以增强特征，并使模型聚焦目标的各个部分，即使它仅在图像中部分可见。在三个遥感目标检测数据集（包括两个公共可用的目标检测数据集（包括两个）的检测结果和消融研究证明了我们方法的有效性，尤其是对于闭塞飞机目标。此外，我们的方法在这些数据集上优于其他最先进的检测方法。

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