Anomaly detection, or outlier detection, is a crucial task in various domains to identify instances that significantly deviate from established patterns or the majority of data. In the context of autonomous driving, the identification of anomalies is particularly important to prevent safety-critical incidents, as deep learning models often exhibit overconfidence in anomalous or outlier samples. In this study, we explore different strategies for training an image semantic segmentation model with an anomaly detection module. By introducing modifications to the training stage of the state-of-the-art DenseHybrid model, we achieve significant performance improvements in anomaly detection. Moreover, we propose a simplified detector that achieves comparable results to our modified DenseHybrid approach, while also surpassing the performance of the original DenseHybrid model. These findings demonstrate the efficacy of our proposed strategies for enhancing anomaly detection in the context of autonomous driving.

翻译：异常检测（或称离群点检测）是多个领域中识别显著偏离正常模式或多数数据实例的关键任务。在自动驾驶背景下，异常识别对预防安全关键事件尤为重要，因为深度学习模型在异常或离群样本上常表现出过度置信。本研究探索了多种训练图像语义分割模型并嵌入异常检测模块的策略。通过对当前最先进的DenseHybrid模型训练阶段进行改进，我们在异常检测性能上取得了显著提升。此外，我们提出了一种简化检测器，该检测器在达到与改进版DenseHybrid模型相当结果的同时，还超越了原始DenseHybrid模型的性能。这些发现证明了我们提出的策略在增强自动驾驶场景下异常检测能力的有效性。