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Traffic Sign Detection and Quality Assessment Using YOLOv8 in Daytime and Nighttime Conditions.

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  • 1Department of Highway and Bridge, Technical College of Engineering, Duhok Polytechnic University, Duhok 1006, Kurdistan Region, Iraq.

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This study enhances traffic sign detection (TSD) and classification (TSC) for autonomous vehicles by evaluating YOLOv8 performance under various conditions. Findings highlight the importance of sign retroreflectivity and material quality for reliable detection, especially at night.

Keywords:
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Area of Science:

  • Computer Vision
  • Road Safety Engineering
  • Artificial Intelligence

Background:

  • Traffic signs are crucial for road safety, but environmental factors degrade their visibility.
  • Compromised visibility affects both human drivers and autonomous vehicle (AV) systems.
  • Existing traffic sign detection (TSD) and classification (TSC) methods face challenges in diverse conditions.

Purpose of the Study:

  • To evaluate the YOLOv8 algorithm's accuracy for traffic sign detection and classification under varied lighting.
  • To investigate the impact of environmental factors and sign material properties on detection performance.
  • To bridge the gap between theoretical TSD research and practical AV system requirements.

Main Methods:

  • Utilized the YOLOv8 algorithm for TSD and TSC on a novel ZND dataset (16,500 images).
  • Conducted complementary retroreflectivity assessments using retroreflectometers.
  • Performed video analysis focusing on image quality (sharpness, brightness, contrast) and human evaluations.

Main Results:

  • YOLOv8 demonstrated robust performance metrics in both day and night scenarios.
  • Sign retroreflectivity significantly correlated with detection performance, particularly at night.
  • Image quality factors like sharpness, brightness, and contrast influenced detection rates.

Conclusions:

  • Advanced algorithms like YOLOv8 are effective for TSD and TSC.
  • Retroreflective quality and material properties are critical for reliable sign detection.
  • A multi-faceted approach combining advanced algorithms, material science, and regular maintenance is essential for improving road safety and AV systems.