Bridge-over-water detection plays vital role in urban surveillance and military reconnaissance. Bridges have arbitrary orientations and extreme aspect ratios in remote sensing images, and the preceding works cannot adequately extract bridge-related features. Small bridges are difficult to detect accurately in optical remote sensing images. The oriented bounding box annotations are required by previous deep-learning-based methods for detecting rotated objects. But obtaining the annotations is a laborious task. Though widely studied previously, they are still challenging problems. To address these problems, modulated deformable convolution and attention mechanisms were introduced in this paper. Modulated deformable convolution made the receptive field more flexible. The feature extraction capability of the network was enhanced. A new weighted structure was designed to quantify the contributions of channel and spatial attention mechanisms. A selective attention usage strategy was proposed to improve the detection performance. To locate bridge-over-water more precisely, a new bounding box conversion module was presented. There was no need for oriented bounding box annotations, and the process only relied on bridge-related prior knowledge. Multiple experiments were performed to verify the effectiveness of proposed methods.

水上桥梁检测在城市监视和军事侦察中起着至关重要的作用。桥梁在遥感图像中具有任意方向和极端纵横比，之前的工作无法充分提取与桥梁相关的特征。在光学遥感图像中，小桥梁难以准确检测。先前基于深度学习的检测旋转对象的方法需要定向边界框注释。但是获取注释是一项艰巨的任务。尽管以前进行了广泛的研究，但它们仍然是具有挑战性的问题。为了解决这些问题，本文引入了调制可变形卷积和注意力机制。调制的可变形卷积使感受野更加灵活。增强了网络的特征提取能力。设计了一种新的加权结构来量化通道和空间注意机制的贡献。提出了一种选择性注意使用策略来提高检测性能。为了更精确地定位水上桥梁，提出了一种新的边界框转换模块。不需要定向边界框注释，该过程仅依赖于与桥梁相关的先验知识。进行了多次实验以验证所提出方法的有效性。并且该过程仅依赖于与桥梁相关的先验知识。进行了多次实验以验证所提出方法的有效性。并且该过程仅依赖于与桥梁相关的先验知识。进行了多次实验以验证所提出方法的有效性。