State-of-the-art deep neural networks demonstrate outstanding performance in semantic segmentation. However, their performance is tied to the domain represented by the training data. Open world scenarios cause inaccurate predictions which is hazardous in safety relevant applications like automated driving. In this work, we enhance semantic segmentation predictions using monocular depth estimation to improve segmentation by reducing the occurrence of non-detected objects in presence of domain shift. To this end, we infer a depth heatmap via a modified segmentation network which generates foreground-background masks, operating in parallel to a given semantic segmentation network. Both segmentation masks are aggregated with a focus on foreground classes (here road users) to reduce false negatives. To also reduce the occurrence of false positives, we apply a pruning based on uncertainty estimates. Our approach is modular in a sense that it post-processes the output of any semantic segmentation network. In our experiments, we observe less non-detected objects of most important classes and an enhanced generalization to other domains compared to the basic semantic segmentation prediction.
翻译:最先进的深层神经网络显示在语义分离方面的杰出性能。 但是,它们的性能与培训数据所代表的领域相联。 开放世界情景造成不准确的预测,在自动驾驶等安全相关应用中,这种预测很危险。 在这项工作中,我们用单层深度估计加强语义分离预测,通过减少在域转移时未探测到的物体的发生来改善分解。 为此,我们通过经修改的分层网络推断出一个深度热映射,这种分层网络产生地表- 地下面罩,与特定的语义分割网络平行运行。 两种分层遮射面都以地表层分类为重点( 道路使用者), 以减少虚假的负差。 为了减少假正差的发生, 我们采用基于不确定性估计的划线。 我们的方法是模块化的, 因为它在处理任何语义分层分割网络的输出时, 。 在我们的实验中, 我们观察到最重要的类中未探测到的物体较少, 并且比基本的语义分段的预测更接近于其他区域。