  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.
