Anomaly segmentation plays a crucial role in identifying anomalous objects within images, which facilitates the detection of road anomalies for autonomous driving. Although existing methods have shown impressive results in anomaly segmentation using synthetic training data, the domain discrepancies between synthetic training data and real test data are often neglected. To address this issue, the Multi-Granularity Cross-Domain Alignment (MGCDA) framework is proposed for anomaly segmentation in complex driving environments. It uniquely combines a new Multi-source Domain Adversarial Training (MDAT) module and a novel Cross-domain Anomaly-aware Contrastive Learning (CACL) method to boost the generality of the model, seamlessly integrating multi-domain data at both scene and sample levels. Multi-source domain adversarial loss and a dynamic label smoothing strategy are integrated into the MDAT module to facilitate the acquisition of domain-invariant features at the scene level, through adversarial training across multiple stages. CACL aligns sample-level representations with contrastive loss on cross-domain data, which utilizes an anomaly-aware sampling strategy to efficiently sample hard samples and anchors. The proposed framework has decent properties of parameter-free during the inference stage and is compatible with other anomaly segmentation networks. Experimental conducted on Fishyscapes and RoadAnomaly datasets demonstrate that the proposed framework achieves state-of-the-art performance.

翻译：异常分割在识别图像中的异常物体方面起着关键作用，有助于自动驾驶中道路异常的检测。尽管现有方法在使用合成训练数据进行异常分割时已展现出优异结果，但合成训练数据与真实测试数据之间的域差异常被忽视。为解决此问题，本文提出多粒度跨域对齐（MGCDA）框架用于复杂驾驶环境下的异常分割。该框架创新性地结合了新型多源域对抗训练（MDAT）模块和跨域异常感知对比学习（CACL）方法，以增强模型泛化能力，并在场景和样本级别无缝整合多域数据。MDAT模块集成了多源域对抗损失和动态标签平滑策略，通过多阶段对抗训练促进场景级域不变特征的获取。CACL利用对比损失对齐跨域数据的样本级表示，并采用异常感知采样策略高效采样困难样本和锚点。所提框架在推理阶段具有免参数特性，且兼容其他异常分割网络。在Fishyscapes和RoadAnomaly数据集上的实验表明，该框架实现了最先进性能。