Accurate and effective object detection in remote sensing images plays an extremely important role in marine transport, environmental monitoring and military operations. Due to the powerful ability of feature representation, region-based convolutional neural networks (RCNNs) have been widely used in this field, which firstly generate candidate regions through extracted feature maps and then classify and locate objects. However, most of existing methods generally use traditional backbone networks to extract feature maps with a decreased spatial resolution because of the continuous down-sampling, which will weaken the information detected from small objects. Besides, sliding windows strategy is employed in these methods to generate fixed anchors with a preset scale on feature maps, which is inappropriate for multi-scale object detection in remote sensing images. To solve the above problems, a novel and effective object detection framework named DetNet-FPN (Feature Pyramid Network) is proposed in this paper, in which a feature pyramid with strong feature representation is created by combining feature maps of different spatial resolution, at the same time, the resolution of feature maps is maintained by involving dilation convolutions. Furthermore, to match the proposed backbone, the GA (Guided Anchoring)-RPN strategy is improved for adaptive anchor generation, this strategy simultaneously predicts the locations where the center of objects are likely to exist as well as the scales and aspect ratios at different locations. Extensive experiments and comprehensive evaluations demonstrate the effectiveness of the proposed framework on DOTA and NWPU VHR-10 datasets.

中文翻译：

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用于遥感图像中多尺度目标检测的自适应锚网络


遥感图像中准确有效的目标检测在海洋运输、环境监测和军事行动中发挥着极其重要的作用。由于强大的特征表示能力，基于区域的卷积神经网络（RCNN）在该领域得到了广泛的应用，它首先通过提取的特征图生成候选区域，然后对对象进行分类和定位。然而，大多数现有方法通常使用传统的骨干网络来提取由于连续下采样而降低了空间分辨率的特征图，这会削弱从小物体中检测到的信息。此外，这些方法采用滑动窗口策略在特征图上生成具有预设比例的固定锚点，这不适合遥感图像中的多尺度目标检测。为了解决上述问题，本文提出了一种新颖有效的目标检测框架DetNet-FPN（特征金字塔网络），其中通过组合不同空间分辨率的特征图创建具有强特征表示的特征金字塔，在同时，通过涉及扩张卷积来维持特征图的分辨率。此外，为了匹配所提出的主干网，改进了 GA（Guided Anchoring）-RPN 策略以实现自适应锚点生成，该策略同时预测对象中心可能存在的位置以及不同位置的尺度和纵横比。大量的实验和综合评估证明了所提出的框架在 DOTA 和 NWPU VHR-10 数据集上的有效性。