论文标题
通过将深度和重建嵌入域适应中，在有雾场景中检测对象检测
Object Detection in Foggy Scenes by Embedding Depth and Reconstruction into Domain Adaptation
论文作者
论文摘要
大多数现有的域适应（DA）方法基于域特征分布将功能对齐，而忽略与雾，背景和目标对象相关的方面，从而呈现次优性能。在我们的DA框架中，我们保留域特征对齐期间的深度和背景信息。引入了生成的深度和雾传变图之间的一致性损失，以增强对齐特征中深度信息的保留。要解决DA过程中潜在生成的错误对象特征，我们提出了一个编码器框架以重建无雾背景图像。这种重建损失还加强了编码器，即我们的DA骨架，以最大程度地减少错误的对象功能。此外，我们以半固定的方式涉及培训我们的DA模块和检测模块，以便我们的检测模块也将其检测模块暴露于未标记的目标数据类型中，该数据类型在测试阶段中使用的数据类型。使用这些想法，我们的方法极大地胜过最先进的方法（47.6映射在Foggy CityScapes数据集上的44.3地图），并在多个实数公共数据集中获得了最佳性能。代码可在以下网址找到：https：//github.com/viml-cvdl/object-detection-in-foggy-scenes
Most existing domain adaptation (DA) methods align the features based on the domain feature distributions and ignore aspects related to fog, background and target objects, rendering suboptimal performance. In our DA framework, we retain the depth and background information during the domain feature alignment. A consistency loss between the generated depth and fog transmission map is introduced to strengthen the retention of the depth information in the aligned features. To address false object features potentially generated during the DA process, we propose an encoder-decoder framework to reconstruct the fog-free background image. This reconstruction loss also reinforces the encoder, i.e., our DA backbone, to minimize false object features.Moreover, we involve our target data in training both our DA module and our detection module in a semi-supervised manner, so that our detection module is also exposed to the unlabeled target data, the type of data used in the testing stage. Using these ideas, our method significantly outperforms the state-of-the-art method (47.6 mAP against the 44.3 mAP on the Foggy Cityscapes dataset), and obtains the best performance on multiple real-image public datasets. Code is available at: https://github.com/VIML-CVDL/Object-Detection-in-Foggy-Scenes
