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Hugging Face PapersYechan Kim, JongHyun Park, Dongho Yoon, Namhoon Jung, Moongu Jeon··访问 1

G-MAD: A Game-Based Data Generation Framework for Multi-View RGB-T Aerial Object Detection

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论文信息

  • arXiv ID 2607.19942
  • 作者 Yechan Kim, JongHyun Park, Dongho Yoon, Namhoon Jung, Moongu Jeon
  • 链接 arXiv · PDF · Hugging Face

摘要

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.