Sequential domain adaptive network in objection detection

Recent advancements in object detection models heavily depend on annotated data, which often requires substantial time and resources to gather. To tackle this challenge, the field of unsupervised domain adaptive object detection focuses on adapting detectors trained on a labeled source domain for effective use in an unlabeled target domain. However, this process faces challenges such as high computational demands and instability in training, due to the significant discrepancies between domains. Our paper presents an efficient Sequential Domain Adaptive Network (SDAN) designed to incrementally minimize these domain differences. The network effectively mitigates the disparities in both initial and subsequent phases. We utilize the Fourier transform for spectrum switching, eliminating the need for training in this stage. To further address the issue of domain shift, the model employs unsupervised adversarial learning for the alignment of varying feature distributions. Extensive experiments demonstrate that our model exhibits strong performance against the most advanced domain adaptive object detection models currently available.