The Solution for the GAIIC2024 RGB-TIR object detection Challenge

• URL: http://arxiv.org/abs/2407.03872v1
• Date: Thu, 4 Jul 2024 12:08:36 GMT
• Title: The Solution for the GAIIC2024 RGB-TIR object detection Challenge
• Authors: Xiangyu Wu, Jinling Xu, Longfei Huang, Yang Yang,
• Abstract summary: RGB-TIR object detection aims to utilize both RGB and TIR images for complementary information during detection. Our proposed method achieved an mAP score of 0.516 and 0.543 on A and B benchmarks respectively.
• Score: 5.625794757504552
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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