Impact of Environmental Conditions on YOLOv8-Based Traffic Sign Detection: A Controlled Comparison of Simulated and Real Weather Cases

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Robust traffic sign detection is essential for reliable autonomous driving systems; however, detection performance can be substantially affected by adverse environmental conditions. Although simulation-based approaches are widely used to evaluate robustness, the extent to which simulated weather reproduces real-world environmental effects remains insufficiently understood. This study presents a controlled, comparative evaluation methodology for assessing a YOLOv8-based traffic sign detection model across four environmental conditions: clear (sunny), simulated rain, real-world rain, and simulated snow. The same road segment, camera configuration, and predefined set of 55 traffic-sign instances were maintained across the evaluated conditions, enabling interpretable comparisons while minimizing scene-level variability. Detection performance was assessed using representative detection confidence (RDC) at the traffic-sign-instance level, along with image-quality metrics such as sharpness, saturation, and intensity. Mean RDC was highest under clear conditions (0.825), followed descriptively by simulated snow (0.647), real-world rain (0.408), and simulated rain (0.395). However, simulated and real-world rain did not differ significantly in RDC (Holm-adjusted p = 0.770), while the Friedman test indicated a significant overall difference among conditions (χ2(3) = 98.767, p < 0.001; Kendall’s W = 0.599). Image-quality analysis further revealed substantial differences between rainfall conditions in successfully detected traffic-sign regions, particularly in Sharpness and Mean Saturation. Overall, the findings demonstrate that simulated weather can produce traffic-sign-level detector responses that are statistically comparable to those observed under independently recorded real-world rainfall, while producing substantially different image-level characteristics. The results support the use of controlled weather simulation as a complementary evaluation approach, alongside real-world validation, to investigate the environmental robustness of camera-based traffic-sign detection systems.