Bridge detection in remote sensing images (RSIs) plays a crucial role in various applications, but it poses unique challenges compared to the detection of other objects. In RSIs, bridges exhibit considerable variations in terms of their spatial scales and aspect ratios. Therefore, to ensure the visibility and integrity of bridges, it is essential to perform holistic bridge detection in large-size very-high-resolution (VHR) RSIs. However, the lack of datasets with large-size VHR RSIs limits the deep learning algorithms' performance on bridge detection. Due to the limitation of GPU memory in tackling large-size images, deep learning-based object detection methods commonly adopt the cropping strategy, which inevitably results in label fragmentation and discontinuous prediction. To ameliorate the scarcity of datasets, this paper proposes a large-scale dataset named GLH-Bridge comprising 6,000 VHR RSIs sampled from diverse geographic locations across the globe. These images encompass a wide range of sizes, varying from 2,048*2,048 to 16,38*16,384 pixels, and collectively feature 59,737 bridges. Furthermore, we present an efficient network for holistic bridge detection (HBD-Net) in large-size RSIs. The HBD-Net presents a separate detector-based feature fusion (SDFF) architecture and is optimized via a shape-sensitive sample re-weighting (SSRW) strategy. Based on the proposed GLH-Bridge dataset, we establish a bridge detection benchmark including the OBB and HBB tasks, and validate the effectiveness of the proposed HBD-Net. Additionally, cross-dataset generalization experiments on two publicly available datasets illustrate the strong generalization capability of the GLH-Bridge dataset.

翻译：桥梁检测在遥感图像中对于多种应用至关重要，但与其他目标检测相比具有独特挑战。在遥感图像中，桥梁在空间尺度和长宽比方面存在显著差异。因此，为确保桥梁的可见性和完整性，必须在大型超高分辨率遥感影像中进行桥梁整体检测。然而，缺乏大尺寸超高分辨率遥感影像数据集限制了深度学习算法在桥梁检测中的性能。由于GPU内存在处理大尺寸图像时存在局限性，基于深度学习的物体检测方法通常采用裁剪策略，这不可避免地导致标签碎片化和预测不连续。为解决数据集稀缺问题，本文提出了名为GLH-Bridge的大规模数据集，包含来自全球不同地理位置的6,000张超高分辨率遥感影像，这些图像尺寸范围从2,048×2,048像素到16,384×16,384像素不等，共包含59,737座桥梁。此外，我们提出了一种用于大尺寸遥感影像中桥梁整体检测的高效网络HBD-Net。HBD-Net采用了基于分离检测器的特征融合架构，并通过形状敏感样本重加权策略进行优化。基于所提出的GLH-Bridge数据集，我们建立了包含有向边界框和水平边界框任务的桥梁检测基准，并验证了所提HBD-Net的有效性。此外，在两个公开数据集上的跨数据集泛化实验表明，GLH-Bridge数据集具有较强的泛化能力。