Multi-domain Dynamic Mean Teacher for Object Detection in Complex Weather

The performance of existing deep learning-based object detection models significantly degrades due to the influence of complex weather. To effectively eliminate the problem of domain differences caused by different weather scenes, propose a multi-domain dynamic mean teacher model. First, introduce a multi-domain mean teacher module to generate pseudo-labels for target domain data in multiple different weather scenes. Then, introduce a student network-based style transfer module to solve the problem of weak generalization ability of student network to different target domains in multi-domain tasks. It can effectively reduce the difference between source domain and different target domains and improve the generalization ability of the student network to different target domains. Finally, a teacher network-based dynamic filtering pseudo-label module is used to dynamically adjust the threshold values of filtering pseudo labels according to the learning effect of the teacher network on different target domains and improve the quality of pseudo labels for each target domain. Experiments on FoggyCityscapes & RainCityscapes and Dusk-rain & Night-rain datasets show that the proposed model achieves 40.3% and 31.4% accuracy respectively, and outperforms other comparison methods in complex weather scenes such as rain, fog and night.