论文标题
关于基于激光雷达的语义分割中代表性不足类的校准
On the calibration of underrepresented classes in LiDAR-based semantic segmentation
论文作者
论文摘要
基于深度学习的感知模型的校准在其可靠性中起着至关重要的作用。我们的工作着重于对基于激光雷达的语义细分的几种模型的置信度进行班级评估，目的是提供有关代表性不足类的校准的见解。这些课程通常包括VRU，因此出于安全原因而特别感兴趣。在基于稀疏曲线的指标的帮助下，我们将三个语义分割模型的校准能力与不同的建筑概念进行了比较，每个概念都以确定性和概率版本为单位。通过识别和描述类别的预测性能与各自的校准质量之间的依赖性，我们旨在促进模型选择和对安全至关重要应用的改进。
The calibration of deep learning-based perception models plays a crucial role in their reliability. Our work focuses on a class-wise evaluation of several model's confidence performance for LiDAR-based semantic segmentation with the aim of providing insights into the calibration of underrepresented classes. Those classes often include VRUs and are thus of particular interest for safety reasons. With the help of a metric based on sparsification curves we compare the calibration abilities of three semantic segmentation models with different architectural concepts, each in a in deterministic and a probabilistic version. By identifying and describing the dependency between the predictive performance of a class and the respective calibration quality we aim to facilitate the model selection and refinement for safety-critical applications.
