Abstract
With the emergence of transformer-based architectures and large language models (LLMs), the accuracy of road scene perception has substantially advanced. Nonetheless, current road scene segmentation approaches are predominantly trained on closed-set data, resulting in insufficient detection capabilities for out-of-distribution (OOD) objects. To overcome this limitation, road anomaly detection methods have been proposed. However, existing methods primarily depend on image inpainting and OOD distribution detection techniques, facing two critical issues: (1) inadequate consideration of the objectiveness attributes of anomalous regions, causing incomplete segmentation when anomalous objects share similarities with known classes, and (2) insufficient attention to environmental constraints, leading to the detection of anomalies irrelevant to autonomous driving tasks. In this paper, we propose a novel framework termed Segmenting Objectiveness and Task-Awareness (SOTA) for autonomous driving scenes. Specifically, SOTA enhances the segmentation of objectiveness through a Semantic Fusion Block (SFB) and filters anomalies irrelevant to road navigation tasks using a Scene-understanding Guided Prompt-Context Adaptor (SG-PCA). Extensive empirical evaluations on multiple benchmark datasets, including Fishyscapes Lost and Found, Segment-Me-If-You-Can, and RoadAnomaly, demonstrate that the proposed SOTA consistently improves OOD detection performance across diverse detectors, achieving robust and accurate segmentation outcomes.
Abstract (translated)
随着基于变压器架构和大型语言模型（LLM）的出现，道路场景感知的准确性得到了显著提升。然而，目前的道路场景分割方法主要是在封闭集数据上进行训练的，这导致了在处理分布外（OOD）对象时检测能力不足的问题。为了解决这一限制，提出了道路异常检测方法。但是，现有的方法主要依赖于图像修复和OOD分布检测技术，面临着两个关键问题：（1）对异常区域客观属性考虑不足，在异常物体与已知类别相似的情况下会导致分割不完整；（2）对环境约束的关注不够，导致检测到的异常与自动驾驶任务无关。 本文提出了一种新的框架，称为对象性和任务感知分割（SOTA），用于自动驾驶场景。具体而言，通过语义融合块（Semantic Fusion Block, SFB），SOTA增强了对客观性的分割，并使用基于场景理解引导提示上下文适配器（Scene-understanding Guided Prompt-Context Adaptor, SG-PCA）过滤了与道路导航任务无关的异常。 在Fishyscapes Lost and Found、Segment-Me-If-You-Can 和 RoadAnomaly 等多个基准数据集上的广泛实证评估表明，所提出的SOTA框架能够持续提高各种检测器的OOD检测性能，并实现了稳健且准确的分割结果。
URL
https://arxiv.org/abs/2504.19183
