DOIONLINE 
DOIONLINE Publish In |
International Journal of Advance Computational Engineering and Networking (IJACEN)-IJACEN |
 Journal Home Volume Issue |
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Issue |
Volume-12,Issue-8 ( Aug, 2024 ) | |||||||||
Paper Title |
Weather-Informed Vision Enhancement for Autonomous Vehicles in Adverse Conditions | |||||||||
Author Name |
Emin Bayramov, Zoltan Istenes | |||||||||
Affilition |
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Pages |
1-5 | |||||||||
Abstract |
Providing Advanced Driver Assistance Systems (ADAS) features requires high-quality image data collected by vehicles. However, adverse weather conditions and nighttime significantly degrade image quality, negatively impacting object detection accuracy and model performance for ADAS function- alities. This paper addresses this critical issue by referencing relevant works that have encountered similar challenges. We propose a novel solution that utilizes the vehicle’s GPS location and data collection timestamp to query weather forecast via a weather API. By obtaining precise weather details at the timeandlocationofdatacollection,weenhanceimagequalitythrough a pre-processing step tailored to the specific weather conditions. UsingtheDAWN(DetectioninAdverseWeatherNature)dataset, our approach demonstrates substantial improvements in image clarity and object detection accuracy across various weather scenarios, significantly enhancing the robustness and reliabilityof object detection models for ADAS systems. Keywords - ADAS Features, Image Enhancement, Adverse Weather Conditions, Object Detection, Weather Api | |||||||||
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