TripleA: An Unsupervised Domain Adaptation Framework for Nighttime VRU Detection

Detecting vulnerable road users (VRUs) at night presents significant challenges. Numerous methods rely heavily on annotations, yet the low visibility of nighttime images poses difficulties for labeling. To obviate the need for nighttime annotations, unsupervised domain adaptation manifests as a viable solution. However, existing approaches primarily focus on semantic-level domain gaps, often overlooking pixel-level discrepancies caused by inherent degradations in the nighttime domain. These degradations can impair machine vision and limit detection performance. In this paper, we propose TripleA, an unsupervised domain adaptation framework tailored for nighttime VRU detection. TripleA includes triple alignment. First, it aligns daytime and nighttime images to generate synthetic nighttime images, which are then enhanced for illumination and noise. To remove noise, we introduce an illumination difference-aware denoising network, incorporating a novel pseudo-supervised attention to achieve pixel-wise noise distribution alignment. This alignment is driven by pseudo-ground truth generated through a carefully designed exchange-recombination strategy, facilitating self-supervised training of the denoising network. Additionally, we introduce degradation alignment to ensure domain-invariant degradation encoding, which enhances the network’s robustness for real-world nighttime images. Extensive experiments demonstrate the effectiveness of our framework for nighttime VRU detection, all without the need for annotated nighttime data.