The Solution for the GAIIC2024 RGB-TIR object detection Challenge
<|reference_start|>The Solution for the GAIIC2024 RGB-TIR object detection Challenge: This report introduces a solution to The task of RGB-TIR object detection from the perspective of unmanned aerial vehicles. Unlike traditional object detection methods, RGB-TIR object detection aims to utilize both RGB and TIR images for complementary information during detection. The challenges of RGB-TIR object detection from the perspective of unmanned aerial vehicles include highly complex image backgrounds, frequent changes in lighting, and uncalibrated RGB-TIR image pairs. To address these challenges at the model level, we utilized a lightweight YOLOv9 model with extended multi-level auxiliary branches that enhance the model's robustness, making it more suitable for practical applications in unmanned aerial vehicle scenarios. For image fusion in RGB-TIR detection, we incorporated a fusion module into the backbone network to fuse images at the feature level, implicitly addressing calibration issues. Our proposed method achieved an mAP score of 0.516 and 0.543 on A and B benchmarks respectively while maintaining the highest inference speed among all models.<|reference_end|>
@article{wu2024the,
    title={The Solution for the GAIIC2024 RGB-TIR object detection Challenge},
    author={Xiangyu Wu, Jinling Xu, Longfei Huang, Yang Yang},
    journal={arXiv preprint arXiv:2407.03872},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.03872},
    primaryClass={cs.CV cs.LG}
}