Object detection is a basic and important task in the field of aerial image processing and has gained much attention in computer vision. However, previous aerial image object detection approaches have insufficient use of scene semantic information between different regions of large-scale aerial images. In addition, complex background and scale changes make it difficult to improve detection accuracy. To address these issues, we propose a relationship representation network for object detection in aerial images (RelationRS): 1) Firstly, multi-scale features are fused and enhanced by a dual relationship module (DRM) with conditional convolution. The dual relationship module learns the potential relationship between features of different scales and learns the relationship between different scenes from different patches in a same iteration. In addition, the dual relationship module dynamically generates parameters to guide the fusion of multi-scale features. 2) Secondly, The bridging visual representations module (BVR) is introduced into the field of aerial images to improve the object detection effect in images with complex backgrounds. Experiments with a publicly available object detection dataset for aerial images demonstrate that the proposed RelationRS achieves a state-of-the-art detection performance.
翻译:在航空图像处理领域,物体探测是一项基本和重要的任务,在计算机视野中引起了很大的注意。然而,以往的航空图像物体探测方法没有充分利用不同区域之间大型航空图像的现场语义信息。此外,复杂的背景和规模变化使得难以提高探测准确性。为了解决这些问题,我们提议在航空图像中建立物体探测关系代表网(RelationRS):1),首先,多尺度特征由带有有条件相变的双向关系模块(DRM)结合和增强。双级关系模块了解不同比例特征之间的潜在关系,并学习同一迭代不同场段不同场景之间的关系。此外,双级关系模块动态生成参数,指导多尺度特征的聚合。2,在航空图像领域引入连接视觉显示模块(BVR),以改进具有复杂背景的图像中的物体探测效果。与可供公众查阅的航空图像对象探测数据集的实验表明,拟议的RelationRS实现了状态的探测性能。