  Maintaining road pavement integrity is crucial for ensuring safe and
efficient transportation. Conventional methods for assessing pavement condition
are often laborious and susceptible to human error. This paper proposes
YOLO9tr, a novel lightweight object detection model for pavement damage
detection, leveraging the advancements of deep learning. YOLO9tr is based on
the YOLOv9 architecture, incorporating a partial attention block that enhances
feature extraction and attention mechanisms, leading to improved detection
performance in complex scenarios. The model is trained on a comprehensive
dataset comprising road damage images from multiple countries, including an
expanded set of damage categories beyond the standard four. This broadened
classification range allows for a more accurate and realistic assessment of
pavement conditions. Comparative analysis demonstrates YOLO9tr's superior
precision and inference speed compared to state-of-the-art models like YOLO8,
YOLO9 and YOLO10, achieving a balance between computational efficiency and
detection accuracy. The model achieves a high frame rate of up to 136 FPS,
making it suitable for real-time applications such as video surveillance and
automated inspection systems. The research presents an ablation study to
analyze the impact of architectural modifications and hyperparameter variations
on model performance, further validating the effectiveness of the partial
attention block. The results highlight YOLO9tr's potential for practical
deployment in real-time pavement condition monitoring, contributing to the
development of robust and efficient solutions for maintaining safe and
functional road infrastructure.
