Reliable pedestrian detection represents a crucial step towards automated driving systems. However, the current performance benchmarks exhibit weaknesses. The currently applied metrics for various subsets of a validation dataset prohibit a realistic performance evaluation of a DNN for pedestrian detection. As image segmentation supplies fine-grained information about a street scene, it can serve as a starting point to automatically distinguish between different types of errors during the evaluation of a pedestrian detector. In this work, eight different error categories for pedestrian detection are proposed and new metrics are proposed for performance comparison along these error categories. We use the new metrics to compare various backbones for a simplified version of the APD, and show a more fine-grained and robust way to compare models with each other especially in terms of safety-critical performance. We achieve SOTA on CityPersons-reasonable (without extra training data) by using a rather simple architecture.

翻译：可靠的行人检测是实现自动驾驶系统的关键步骤。然而，现有的性能基准存在缺陷。当前验证数据集各子集所采用的度量标准阻碍了对行人检测深度神经网络进行真实的性能评估。由于图像分割能够提供街道场景的细粒度信息，它可以作为在行人检测器评估过程中自动区分不同类型错误的起点。本研究提出了行人检测的八种不同错误类别，并针对这些错误类别提出了新的性能比较度量标准。我们使用新度量标准比较了简化版APD的各种骨干网络，展示了一种更细粒度且稳健的模型比较方法，特别是在安全关键性能方面。通过采用相对简单的架构，我们在CityPersons-reasonable数据集上（无需额外训练数据）实现了最先进的性能。