G-MAD：基于游戏的多视角RGB-T航空目标检测数据生成框架

摘要

本工作介绍了G-MAD，这是一个开源框架，利用Arma3生成用于航空目标检测的同步多视角RGB-T数据。G-MAD解决了现实航空数据集构建中的关键局限性，包括视角控制受限、RGB-T对齐不完美以及标注成本高昂。该框架支持结构化场景配置、可控多视角相机布设、可见光/热红外同步采集，以及利用引擎级几何元数据的自动边界框标注。这些能力使得能够在航空目标检测中开展视角变化、多模态融合以及合成到真实迁移的受控研究。此外，基于G-MAD，我们构建并发布了AMOD，一个大规模多视角航空RGB-T目标检测新基准。源代码和数据集可在 https://unique-chan.github.io/G-MAD-Project 获取。

English

This work introduces G-MAD, an open-source framework that uses Arma3 to generate synchronized multi-view RGB-T data for aerial object detection. G-MAD addresses key limitations of real-world aerial dataset construction, including limited viewpoint control, imperfect RGB-T alignment and high annotation cost. The framework supports structured scenario specification, controllable multi-view camera placement, simultaneous visible/thermal capture, and automatic bounding box annotation using engine-level geometric metadata. These capabilities enable controlled studies of viewpoint variation, multi-modal fusion, and synthetic-to-real transfer in aerial object detection. Besides, using G-MAD, we construct and release AMOD, a new large-scale multi-view aerial RGB-T object detection benchmark. The source code and the dataset are available at https://unique-chan.github.io/G-MAD-Project.