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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >Object Detection Based on Efficient Multiscale Auto-Inference in Remote Sensing Images
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Object Detection Based on Efficient Multiscale Auto-Inference in Remote Sensing Images

机译：基于高效多尺度自动推理的对象检测遥感图像

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摘要

Object detection in remote sensing images has important applications in various aspects. Object detection algorithms with deep convolutional neural networks (DCNNs) have made remarkable progress. However, when processing objects on vastly multiple scales in high-resolution optical remote sensing images, there is a high computational cost. Therefore, to simplify neural network multiscale training and inference, an automatic multiscale inference framework is proposed to balance the speed and accuracy of object detection. We use an attention mechanism that uses a key-point network to predict regions with small objects on a coarse scale and only process regions obtained from the first stage on finer scales instead of processing an entire larger scale image. The fully convolutional neural network (CNN) that is used in training and detecting is not affected by the image input resolution. The experiments are carried out using the NWPUVHR-10 data set, and the experimental results show that these methods can improve the training efficiency and detection accuracy in remote sensing images.
机译：遥感图像中的对象检测在各个方面具有重要应用。具有深度卷积神经网络（DCNN）的对象检测算法取得了显着的进展。然而，当在高分辨率光学遥感图像中大量尺度上处理对象时，有很高的计算成本。因此，为了简化神经网络多尺度训练和推断，建议建议自动多尺寸推断框架来平衡物体检测的速度和准确性。我们使用注意机制，该注意机制使用关键点网络来预测粗略尺度上具有小对象的区域，并且仅在更精细的尺度上获得的处理区域而不是处理整个更大的尺度图像。用于训练和检测的完全卷积神经网络（CNN）不受图像输入分辨率的影响。实验使用NWPUVHR-10数据集进行，实验结果表明，这些方法可以提高遥感图像中的训练效率和检测精度。

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