This study aims to learn a translation from visible to infrared imagery, bridging the domain gap between the two modalities so as to improve accuracy on downstream tasks including object detection. Previous approaches attempt to perform bi-domain feature fusion through iterative optimization or end-to-end deep convolutional networks. However, we pose the problem as similar to that of image translation, adopting a two-stage training strategy with a Generative Adversarial Network and an object detection model. The translation model learns a conversion that preserves the structural detail of visible images while preserving the texture and other characteristics of infrared images. Images so generated are used to train standard object detection frameworks including Yolov5, Mask and Faster RCNN. We also investigate the usefulness of integrating a super-resolution step into our pipeline to further improve model accuracy, and achieve an improvement of as high as 5.3% mAP.

本研究旨在学习可见光图像与红外图像之间的翻译，以缩小两种模态之间的域间差距，从而提高目标检测等下游任务的准确性。论文提出了一种基于生成对抗网络和目标检测模型的两阶段训练策略，实现了在保持可见光图像结构细节的同时保留红外图像纹理的转换，凭借这一方法，我们在模型准确性上获得了高达5.3%的mAP提升。

可见光到红外域的监督图像翻译用于目标检测