F-LGAM: Enhancing Single Domain Generalized Object Detection Through Fourier-Based Local and Global Amplitude Mixup

This paper addresses the challenge of Single Domain Generalized Object Detection, where a network is trained solely on source images and must accurately detect and classify objects in unseen target domains. To improve object detection across varying weather conditions, it is essential to consider both the local regions where objects are present and the global context of the entire image. In this paper, we introduce a Fourier-Based Local and Global Amplitude MixUp called FLGAM, which uses Fourier transform to augment both the local and global components of a single source image, simulating diverse environmental conditions. By enhancing the training data through this augmentation, the model achieves better generalization to new and unseen environments. Consequently, the proposed approach outperforms other state-of- the-art networks in various weather scenarios encountered in driving situations.