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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >Squeeze and Excitation Rank Faster R-CNN for Ship Detection in SAR Images
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Squeeze and Excitation Rank Faster R-CNN for Ship Detection in SAR Images

机译：SAR图像中的船舶检测挤压和激励等级更快R-CNN

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摘要

Synthetic aperture radar (SAR) ship detection is an important part of marine monitoring. With the development in computer vision, deep learning has been used for ship detection in SAR images such as the faster region-based convolutional neural network (R-CNN), single-shot multibox detector, and densely connected network. In SAR ship detection field, deep learning has much better detection performance than traditional methods on nearshore areas. This is because traditional methods need sea-land segmentation before detection, and inaccurate sea-land mask decreases its detection performance. Though current deep learning SAR ship detection methods still have many false detections in land areas, and some ships are missed in sea areas. In this letter, a new network architecture based on the faster R-CNN is proposed to further improve the detection performance by using squeeze and excitation mechanism. In order to improve performance, first, the feature maps are extracted and concatenated to obtain multiscale feature maps with ImageNet pretrained VGG network. After region of interest pooling, an encoding scale vector which has values between 0 and 1 is generated from subfeature maps. The scale vector is ranked, and only top K values will be preserved. Other values will be set to 0. Then, the subfeature maps are recalibrated by this scale vector. The redundant subfeature maps will be suppressed by this operation, and the detection performance of detector can be improved. The experimental results based on Sentinel-1 images show that the detection performance of the proposed method achieves 0.836 which is 9.7% better than the state-of-the-art method when using Fl as matric and executes 14% faster.
机译：合成孔径雷达（SAR）船舶检测是海洋监测的重要组成部分。随着计算机愿景的发展，深入学习已被用于SAR图像中的船舶检测，例如基于更快的基于区域的卷积神经网络（R-CNN），单次多射门探测器和密集连接的网络。在SAR船舶检测领域，深度学习比近岸地区的传统方法更好地检测性能。这是因为传统方法在检测前需要海上分割，并且不准确的海上掩模会降低其检测性能。虽然目前的深度学习SAR船舶检测方法仍然在土地面积中具有许多错误的检测，但有些船舶在海域错过了。在这封信中，提出了一种基于更快的R-CNN的网络架构，通过使用挤压和激励机制进一步提高检测性能。为了提高性能，首先，提取特征映射并连接，以获取MultiScale Feature映射，其中包含Imagenet Prevary VGG网络。在利息区域之后，从子宫映射图生成具有0和1之间的编码刻度矢量。刻度向量排列，只会保留顶部k值。其他值将设置为0.然后，通过该比例矢量重新校准子处理映射。通过该操作将抑制冗余子处理图，并且可以提高检测器的检测性能。基于Sentinel-1图像的实验结果表明，该方法的检测性能达到0.836，当使用FL作为原始的方法时，比最先进的方法更好，更快地执行14％。

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