Domain Generalised Fully Convolutional One Stage Detection

Real-time vision in robotics plays an important role in localising and recognising objects. Recently, deep learning approaches have been widely used in robotic vision. However, most of these approaches have assumed that training and test sets come from similar data distributions, which is not valid in many real world applications. This study proposes an approach to address domain generalisation (i.e. out-of-distribution generalisation, OODG) where the goal is to train a model via one or more source domains, that will generalise well to unknown target domains using single stage detectors. All existing approaches which deal with OODG either use slow two stage detectors or operate under the covariate shift assumption which may not be useful for real-time robotics. This is the first paper to address domain generalisation in the context of single stage anchor free object detector FCOS without the covariate shift assumption. We focus on improving the generalisation ability of object detection by proposing new regularisation terms to address the domain shift that arises due to both classification and bounding box regression. Also, we include an additional consistency regularisation term to align the local and global level predictions. The proposed approach is implemented as a Domain Generalised Fully Convolutional One Stage (DGFCOS) detection and evaluated using four object detection datasets which provide domain metadata (GWHD, Cityscapes, BDD100K, Sim10K) where it exhibits a consistent performance improvement over the baselines and is able to run in real-time for robotics.