Self-Supervised Saliency Estimation for Pixel Embedding in Road Detection

Abstract

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.