Presenter(s)

    Vatsa Patel, Le Ba Thinh Tran

    Comments

    12:00-12:20, LTC Studio

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    Description

    The performance of object detection models in adverse weather conditions remains a critical challenge for intelligent transportation systems. Since advancements in autonomous driving rely heavily on extensive datasets, which help autonomous driving systems be reliable in complex driving environments, this study provides a comprehensive dataset under diverse weather scenarios like rain, fog, nighttime, or sun flares and systematically evaluates the robustness of state-of-the-art deep learning-based object detection frameworks. Our Adverse Driving Conditions Dataset (ADCD) features eight single-weather effects and four challenging mixed-weather effects, with a curated collection of 50,000 traffic images for each weather effect. State-of-the-art object detection models are evaluated using standard metrics, including precision, recall, and IoU. Our findings reveal significant performance degradation under adverse conditions compared to clear weather, highlighting common issues such as misclassification and false positives. For example, scenarios like fog combined with rain cause frequent detection failures, highlighting the limitations of current algorithms. Through comprehensive performance analysis, we provide critical insights into model vulnerabilities and propose directions for developing weather-resilient object detection systems. This work contributes to advancing robust computer vision technologies for safer and more reliable transportation in unpredictable real-world environments.

    Publication Date

    4-23-2025

    Project Designation

    Independent Research

    Primary Advisor

    Tam Nguyen

    Primary Advisor's Department

    Computer Science

    Keywords

    Stander Symposium, College of Arts and Sciences

    Institutional Learning Goals

    Scholarship; Practical Wisdom; Vocation

    Evaluating AI Vision in Extreme Weather

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