Road detection is an important task inthe signal processing field. Although self-supervised learning has the potential to learn rich and effective visual representations that avoid tedious labeling, the current approaches learn from object-centered images, which leads to ambiguous results in complex traffic scenarios. We introduce saliency estimation to extend the self-supervised segmentation beyond object-center images, with spatial-temporal information and ensemble learning employed to improve the robustness. Then, we also design a quadruple loss for the pixel embedding learning and optimize the affinity between different categories, while exploring structural information in negative pixels. Experiments on the public datasets show that our approach is competitive with state-of-the-art approaches.

中文翻译：

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道路检测中像素嵌入的自监督显着性估计

道路检测是信号处理领域的一项重要任务。尽管自监督学习有可能学习丰富有效的视觉表示，避免繁琐的标记，但当前的方法是从以对象为中心的图像中学习，这在复杂的交通场景中会导致模棱两可的结果。我们引入了显着性估计以将自监督分割扩展到对象中心图像之外，使用时空信息和集成学习来提高鲁棒性。然后，我们还为像素嵌入学习设计了四重损失并优化不同类别之间的亲和力，同时探索负像素中的结构信息。在公共数据集上的实验表明，我们的方法与最先进的方法具有竞争力。