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首页> 外文期刊>Neural computing & applications >Arbitrary-oriented object detection via dense feature fusion and attention model for remote sensing super-resolution image
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Arbitrary-oriented object detection via dense feature fusion and attention model for remote sensing super-resolution image

机译：通过密集特征融合和遥感超分辨率图像的偏心特征融合和注意模型进行任意定向的物体检测

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摘要

In this paper, we aim at developing a new arbitrary-oriented end-to-end object detection method to further push the frontier of object detection for remote sensing image. The proposed method comprehensively takes into account multiple strategies, such as attention mechanism, feature fusion, rotation region proposal as well as super-resolution pre-processing simultaneously to boost the performance in terms of localization and classification under the faster RCNN-like framework. Specifically, a channel attention network is integrated for selectively enhancing useful features and suppressing useless ones. Next, a dense feature fusion network is designed based on multi-scale detection framework, which fuses multiple layers of features to improve the sensitivity to small objects. In addition, considering the objects for detection are often densely arranged and appear in various orientations, we design a rotation anchor strategy to reduce the redundant detection regions. Extensive experiments on two remote sensing public datasets DOTA, NWPU VHR-10 and scene text dataset ICDAR2015 demonstrate that the proposed method can be competitive with or even superior to the state-of-the-art ones, like R2CNN and R2CNN++.
机译：在本文中，我们的目标是开发一种新的任意导向的端到端对象检测方法，以进一步推动对象检测的前沿进行遥感图像。所提出的方法全面考虑了多种策略，例如注意机制，特征融合，旋转区域提案以及超分辨率预处理，同时提高了在更快的RCNN的框架下的定位和分类方面的性能。具体地，集成了通道注意网络，用于选择性地增强有用的特征并抑制无用的功能。接下来，基于多尺度检测框架设计了密集特征融合网络，其熔化多层特征，以提高对小物体的敏感性。另外，考虑到检测的物体通常密集地布置并且以各种方向出现，我们设计旋转锚策略以减少冗余检测区域。在两个遥感公共数据集DOTA，NWPU VHR-10和场景文本数据集ICDAR2015上进行了广泛的实验证明了所提出的方法可以与最先进的方法竞争，例如R2CNN和R2CNN ++。

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