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.

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