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

Shivank Garg, Abhishek Baghel, Amit Agarwal, Durga Toshniwal
6/19/2024
cs.CV

No official code found. Best implementation: abhishekbaghel11/Traffic-light-detection-in-adverse-weather-conditions (1 star, last commit 2024-05-23).

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.

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Code Implementations(1)

abhishekbaghel11/Traffic-light-detection-in-adverse-weather-conditionsCommunity100%

A Robust Model for Traffic Light Detection in Adverse Weather Conditions

12Jupyter NotebookMay 23, 20242 years ago

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Cite this paper

@article{garg2024snowy,
  title  = {Snowy Scenes,Clear Detections: A Robust Model for Traffic Light Detection in Adverse Weather Conditions},
  author = {Shivank Garg and Abhishek Baghel and Amit Agarwal and Durga Toshniwal},
  year   = {2024},
  eprint = {2406.13473},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2406.13473v1}
}

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