Snowy Scenes,Clear Detections: A Robust Model for Traffic Light Detection in Adverse Weather Conditions

TL;DR

This paper presents a robust traffic light detection framework that significantly improves accuracy in adverse weather conditions like snow, rain, and fog, addressing a critical challenge for autonomous driving safety.

Contribution

The paper introduces a novel detection pipeline specifically designed to handle domain shifts caused by adverse weather, outperforming existing methods in challenging conditions.

Findings

40.8% improvement in average IoU and F1 scores

22.4% performance increase in domain shift scenarios

Enhanced detection accuracy in snow, rain, and fog

Abstract

With the rise of autonomous vehicles and advanced driver-assistance systems (ADAS), ensuring reliable object detection in all weather conditions is crucial for safety and efficiency. Adverse weather like snow, rain, and fog presents major challenges for current detection systems, often resulting in failures and potential safety risks. This paper introduces a novel framework and pipeline designed to improve object detection under such conditions, focusing on traffic signal detection where traditional methods often fail due to domain shifts caused by adverse weather. We provide a comprehensive analysis of the limitations of existing techniques. Our proposed pipeline significantly enhances detection accuracy in snow, rain, and fog. Results show a 40.8% improvement in average IoU and F1 scores compared to naive fine-tuning and a 22.4% performance increase in domain shift scenarios, such as training on artificial snow and testing on rain images.