The detection of unknown traffic obstacles is vital to ensure safe autonomous driving. The standard object-detection methods cannot identify unknown objects that are not included under predefined categories. This is because object-detection methods are trained to assign a background label to pixels corresponding to the presence of unknown objects. To address this problem, the pixel-wise anomaly-detection approach has attracted increased research attention. Anomaly-detection techniques, such as uncertainty estimation and perceptual difference from reconstructed images, make it possible to identify pixels of unknown objects as out-of-distribution (OoD) samples. However, when applied to images with many unknowns and complex components, such as driving scenes, these methods often exhibit unstable performance. The purpose of this study is to achieve stable performance for detecting unknown objects by incorporating the object-detection fashions into the pixel-wise anomaly detection methods. To achieve this goal, we adopt a semantic-segmentation network with a sigmoid head that simultaneously provides pixel-wise anomaly scores and objectness scores. Our experimental results show that the objectness scores play an important role in improving the detection performance. Based on these results, we propose a novel anomaly score by integrating these two scores, which we term as unknown objectness score. Quantitative evaluations show that the proposed method outperforms state-of-the-art methods when applied to the publicly available datasets.

翻译：未知交通障碍物的检测对于确保自动驾驶安全性至关重要。标准的目标检测方法无法识别未包含在预定义类别中的未知物体，这是因为目标检测方法训练过程中会将对应未知物体存在的像素赋予背景标签。为解决此问题，基于像素的异常检测方法受到了越来越多的研究关注。不确定性估计与重建图像的感知差异等异常检测技术，使得将未知物体像素识别为分布外样本成为可能。然而，当应用于包含大量未知元素及复杂成分（如驾驶场景）的图像时，这些方法常表现出不稳定的性能。本研究旨在通过将目标检测范式融入基于像素的异常检测方法，实现未知物体的稳定检测。为此，我们采用配备Sigmoid头的语义分割网络，该网络可同时提供逐像素的异常分数与目标性分数。实验结果表明，目标性分数在提升检测性能中发挥重要作用。基于此，我们通过融合这两类分数提出了一种新型异常分数，并将其命名为未知目标性分数。定量评估显示，在公开数据集上，所提方法优于现有最优方法。