Unsupervised Domain Adaptive Lane Detection via Contextual Contrast and Aggregation

TL;DR

This paper introduces DACCA, a novel unsupervised domain adaptive lane detection method that leverages contextual contrast and feature aggregation to improve cross-domain performance.

Contribution

It proposes a domain-adaptive lane detection approach using cross-domain contrastive loss and feature aggregation, addressing feature discrimination and context transfer issues.

Findings

DACCA outperforms existing methods on six datasets.

Achieves 92.10% accuracy transferring from CULane to Tusimple.

Significantly improves cross-domain lane detection performance.

Abstract

This paper focuses on two crucial issues in domain-adaptive lane detection, i.e., how to effectively learn discriminative features and transfer knowledge across domains. Existing lane detection methods usually exploit a pixel-wise cross-entropy loss to train detection models. However, the loss ignores the difference in feature representation among lanes, which leads to inefficient feature learning. On the other hand, cross-domain context dependency crucial for transferring knowledge across domains remains unexplored in existing lane detection methods. This paper proposes a method of Domain-Adaptive lane detection via Contextual Contrast and Aggregation (DACCA), consisting of two key components, i.e., cross-domain contrastive loss and domain-level feature aggregation, to realize domain-adaptive lane detection. The former can effectively differentiate feature representations among categories by taking domain-level features as positive samples. The latter fuses the domain-level and pixel-level features to strengthen cross-domain context dependency. Extensive experiments show that DACCA significantly improves the detection model's performance and outperforms existing unsupervised domain adaptive lane detection methods on six datasets, especially achieving the best performance when transferring from CULane to Tusimple (92.10% accuracy), Tusimple to CULane (41.9% F1 score), OpenLane to CULane (43.0% F1 score), and CULane to OpenLane (27.6% F1 score).