YOLO9tr: a lightweight model for pavement damage detection utilizing a generalized efficient layer aggregation network and attention mechanism

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. This dataset includes an expanded set of damage categories beyond the standard four types (longitudinal cracks, transverse cracks, alligator cracks, and potholes), providing a more nuanced classification of road damage. 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 YOLOv8, YOLOv9 and YOLOv10, 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.