  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.
