Single-source remote sensing object detection using optical or SAR images struggles in complex environments. Optical images offer rich textural details but are often affected by low-light, cloud-obscured, or low-resolution conditions, reducing the detection performance. SAR images are robust to weather, but suffer from speckle noise and limited semantic expressiveness. Optical and SAR images provide complementary advantages, and fusing them can significantly improve the detection accuracy. However, progress in this field is hindered by the lack of large-scale, standardized datasets. To address these challenges, we propose the first comprehensive dataset for optical-SAR fusion object detection, named Multi-resolution, Multi-polarization, Multi-scene, Multi-source SAR dataset (M4-SAR). It contains 112,184 precisely aligned image pairs and nearly one million labeled instances with arbitrary orientations, spanning six key categories. To enable standardized evaluation, we develop a unified benchmarking toolkit that integrates six state-of-the-art multi-source fusion methods. Furthermore, we propose E2E-OSDet, a novel end-to-end multi-source fusion detection framework that mitigates cross-domain discrepancies and establishes a robust baseline for future studies. Extensive experiments on M4-SAR demonstrate that fusing optical and SAR data can improve $mAP$ by 5.7\% over single-source inputs, with particularly significant gains in complex environments. The dataset and code are publicly available at https://github.com/wchao0601/M4-SAR.

翻译：基于单一源（光学或SAR）的遥感目标检测在复杂环境中面临挑战。光学图像提供丰富的纹理细节，但常受光照不足、云层遮挡或分辨率低等条件影响，导致检测性能下降。SAR图像对天气条件具有鲁棒性，但存在斑点噪声且语义表达能力有限。光学与SAR图像具有互补优势，融合二者可显著提升检测精度。然而，该领域的发展因缺乏大规模、标准化的数据集而受到阻碍。为应对这些挑战，我们提出了首个用于光学-SAR融合目标检测的综合数据集，命名为多分辨率、多极化、多场景、多源SAR数据集（M4-SAR）。该数据集包含112,184对精确配准的图像对以及近百万个任意朝向的标注实例，涵盖六个关键类别。为实现标准化评估，我们开发了一个统一的基准测试工具包，集成了六种先进的多源融合方法。此外，我们提出了E2E-OSDet，一种新颖的端到端多源融合检测框架，该框架能缓解跨域差异，并为未来研究建立了一个稳健的基线。在M4-SAR上进行的大量实验表明，融合光学与SAR数据可将$mAP$较单源输入提升5.7\%，在复杂环境中增益尤为显著。数据集与代码已公开于https://github.com/wchao0601/M4-SAR。