M4-SAR: A Multi-Resolution, Multi-Polarization, Multi-Scene, Multi-Source Dataset and Benchmark for optical-SAR Object Detection

Single-source remote sensing object detection using opticalor SAR images struggles in complex environments. Optical images offerrich textural details but are often affected by low-light, cloud-obscured,or low-resolution conditions, reducing the detection performance. SARimages are robust to weather, but suffer from speckle noise and limitedsemantic expressiveness. Optical and SAR images provide complemen-tary advantages, and fusing them can significantly improve the detectionaccuracy. However, progress in this field is hindered by the lack of large-scale, standardized datasets. To address these challenges, we proposea new comprehensive dataset for optical-SAR fusion object detection,named Multi-resolution, Multi-polarization, Multi-scene, Multi-sourceSAR dataset (M4-SAR). It contains 112,174 instance-level aligned im-age pairs and nearly one million labeled instances with arbitrary orienta-tions, spanning six key categories. To enable standardized evaluation, wedevelop a unified benchmarking toolkit that integrates six state-of-the-art multi-source fusion methods. Additionally, we propose E2E-OSDet, anovel end-to-end multi-source fusion detection framework that mitigatescross-domain discrepancies and establishes a robust baseline for futurestudies. Extensive experiments on M4-SAR demonstrate that fusing op-tical and SAR data can improve mAP by 5.7% over single-source inputs,with particularly significant gains in complex environments.