Multi-Target Domain Transfer Detector on Bad Weather Conditions

Abstract

With the popularization of object detection in fields such as autonomous driving and surveillance, object detection networks are required to be used in more changeable scenarios. However, a multi-target domain adaptive object detection network is a challenging problem. Due to the lack of relevant datasets and the limitation of traditional domain adaptive methods, there is a lack of pertinent research in this field. Traditional single-target domain adaptive methods are often ineffective when used in multi-target domain adaptive problems. To allow the object detection network to be applied to multiple target domains, we propose the “domain transfer module” and the “multi-scale hybrid attention domain alignment module”. At the same time, we synthesized the “BlendedCityspce” dataset for training and testing of a multi-target domain adaptive target object network. The network provided in this paper has a good performance in the multi-target domain adaptation and reaches the state-of-the-art in the single-target domain adaptation. You can find our source code in https://github.com/Chonghuan-Liu/Multi-Target-Domain-Adaptative-Detector.