G-MAD: 基於遊戲的多視角RGB-T航空目標檢測數據生成框架

摘要

本工作介紹了G-MAD，這是一個開源框架，利用Arma3生成同步多視角RGB-T數據，應用於空中目標偵測。G-MAD解決了真實空中數據集建構的主要限制，包括有限的視角控制、不完美的RGB-T對齊以及高昂的標註成本。該框架支援結構化場景規範、可控的多視角相機佈置、同步可見光/熱成像擷取，以及利用引擎層級的幾何元資料進行自動邊界框標註。這些功能使得在空中目標偵測中，能夠進行視角變化、多模態融合以及合成到真實遷移的受控研究。此外，利用G-MAD，我們建構並發佈了AMOD，這是一個全新的大規模多視角空中RGB-T目標偵測基準。原始碼與數據集可於https://unique-chan.github.io/G-MAD-Project取得。

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.