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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >Multi-Scale Spatial and Channel-wise Attention for Improving Object Detection in Remote Sensing Imagery
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Multi-Scale Spatial and Channel-wise Attention for Improving Object Detection in Remote Sensing Imagery

机译：用于改善遥感图像的对象检测的多尺度空间和通道的关注

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摘要

The spatial resolution of remote sensing images is continuously improved by the development of remote sensing satellite and sensor technology. Hence, background information in an image becomes increasingly complex and causes considerable interference to the object detection task. Can we pay as much attention to the object in an image as human vision does? This letter proposes a multi-scale spatial and channel-wise attention (MSCA) mechanism to answer this question. MSCA has two advantages that help improve object detection performance. First, attention is paid to the spatial area related to the foreground, and compared with other channels, more attention is given to the feature channel with a greater response to the foreground region. Second, for objects with different scales, MSCA can generate an attention distribution map that integrates multi-scale information and applies it to the feature map of the deep network. MSCA is a flexible module that can be easily embedded into any object detection model based on deep learning. With the attention exerted by MSCA, the deep neural network can efficiently focus on objects of different backgrounds and sizes in remote sensing images. Experiments show that the mean average precision of object detection is improved after the addition of MSCA to the current object detection model.
机译：通过开发遥感卫星和传感器技术的开发不断改善遥感图像的空间分辨率。因此，图像中的背景信息变得越来越复杂，并对对象检测任务产生相当大的干扰。我们可以随着人类愿景表达图像中的对象吗？这封信提出了多种空间和渠道的关注（MSCA）机制来回答这个问题。 MSCA有两个优点，有助于提高对象检测性能。首先，注意与前景有关的空间区域，与其他通道相比，对特征频道的更大关注具有更大的前景区域。其次，对于具有不同尺度的对象，MSCA可以生成关注分布图，该地图集成了多尺度信息，并将其应用于深网络的特征映射。 MSCA是一种灵活的模块，可以轻松嵌入基于深度学习的任何对象检测模型。由于MSCA施加的注意，深神经网络可以有效地关注遥感图像中不同背景和大小的对象。实验表明，在向当前物体检测模型中加入MSCA后，物体检测的平均平均精度得到改善。

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