论文标题
使用深度估计的域移动下的语义分割的假阴性减少
False Negative Reduction in Semantic Segmentation under Domain Shift using Depth Estimation
论文作者
论文摘要
最新的深神经网络在语义细分方面表现出了出色的性能。但是，它们的性能与培训数据所代表的领域相关。开放世界的场景会导致预测不准确，这在安全相关应用中是危险的，例如自动驾驶。在这项工作中，我们使用单眼深度估计来增强语义分割预测，从而通过减少存在域移位的情况下的未检测对象的发生来改善分割。为此，我们通过修改后的分割网络推断出深度热图，该网络生成前景掩模，该掩模与给定的语义分割网络并行运行。两种细分面具均汇总，重点关注前景类别（此处的道路使用者），以减少虚假负面因素。为了减少假阳性的发生，我们根据不确定性估计进行修剪。从某种意义上说，我们的方法是模块化的，它后处理了任何语义分割网络的输出。在我们的实验中，与基本的语义分割预测相比，我们观察到大多数重要类别的未检测到的对象，并增强对其他领域的概括。
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.
