This paper dives into the cutting-edge world of road asset detection on Thai highways, showcasing a novel approach that combines an upgraded REG model with Generalized Focal Loss. Our focus is on identifying key road elements—like pavilions, pedestrian bridges, information and warning signs, and concrete guardrails—to boost road safety and infrastructure management. While deep learning methods have shown promise, traditional models often struggle with accuracy in tricky conditions, such as cluttered backgrounds and variable lighting. To tackle these issues, we’ve integrated REG with Generalized Focal Loss, enhancing its ability to detect road assets with greater precision. Our results are impressive, the REGx model led the way with a mAP50 of 80.340, mAP50-95 of 60.840, precision of 79.100, recall of 76.680, and an F1-score of 77.870. These findings highlight the REGx model’s superior performance, demonstrating the power of advanced deep learning techniques to improve highway safety and infrastructure maintenance, even in challenging conditions.
