# Multi-Target Domain Adaptation via Unsupervised Domain Classification for Weather Invariant Object Detection Source: [Multi-Target Domain Adaptation via Unsupervised Domain Classification for Weather Invariant Object Detection]() Collection index: [llms.txt]() Data updated: 2025-10-28T13:56:47.021Z Authors: Ting Sun, Jinlin Chen, Francis Ng Published: 2021-03-25T16:59:35.000Z ## Abstract Object detection is an essential technique for autonomous driving. The performance of an object detector significantly degrades if the weather of the training images is different from that of test images. Domain adaptation can be used to address the domain shift problem so as to improve the robustness of an object detector. However, most existing domain adaptation methods either handle single target domain or require domain labels. We propose a novel unsupervised domain classification method which can be used to generalize single-target domain adaptation methods to multi-target domains, and design a weather-invariant object detector training framework based on it. We conduct the experiments on Cityscapes dataset and its synthetic variants, i.e. foggy, rainy, and night. The experimental results show that the object detector trained by our proposed method realizes robust object detection under different weather conditions. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()