Fourier Domain Adaptation for Traffic Light Detection in Adverse Weather

TL;DR

This paper introduces Fourier Domain Adaptation (FDA), a method that improves traffic light detection in adverse weather by reducing domain gaps without changing model architecture, leading to significant performance gains.

Contribution

The paper presents FDA, a novel data modification technique that enhances traffic light detection under adverse weather without increasing model complexity.

Findings

FDA-augmented models outperform baselines in key metrics

YOLOv8 shows a 12.25% average increase across metrics

Significant improvements in Precision, Recall, and mAP achieved

Abstract

Traffic light detection under adverse weather conditions remains largely unexplored in ADAS systems, with existing approaches relying on complex deep learning methods that introduce significant computational overheads during training and deployment. This paper proposes Fourier Domain Adaptation (FDA), which requires only training data modifications without architectural changes, enabling effective adaptation to rainy and foggy conditions. FDA minimizes the domain gap between source and target domains, creating a dataset for reliable performance under adverse weather. The source domain merged LISA and S2TLD datasets, processed to address class imbalance. Established methods simulated rainy and foggy scenarios to form the target domain. Semi-Supervised Learning (SSL) techniques were explored to leverage data more effectively, addressing the shortage of comprehensive datasets and poor performance of state-of-the-art models under hostile weather. Experimental results show FDA-augmented models outperform baseline models across mAP50, mAP50-95, Precision, and Recall metrics. YOLOv8 achieved a 12.25% average increase across all metrics. Average improvements of 7.69% in Precision, 19.91% in Recall, 15.85% in mAP50, and 23.81% in mAP50-95 were observed across all models, demonstrating FDA's effectiveness in mitigating adverse weather impact. These improvements enable real-world applications requiring reliable performance in challenging environmental conditions.