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.

翻译：保持路面完整性对于确保安全高效的交通运输至关重要。传统的路面状况评估方法通常耗时费力且易受人为误差影响。本文提出YOLO9tr，一种基于深度学习技术的新型轻量化目标检测模型，用于路面损伤检测。YOLO9tr以YOLOv9架构为基础，引入部分注意力模块以增强特征提取与注意力机制，从而提升复杂场景下的检测性能。该模型在包含多国道路损伤图像的综合性数据集上进行训练，其损伤类别涵盖范围超越标准四分类体系。这种扩大的分类范围能够实现更精确、更贴近实际的路面状况评估。对比分析表明，相较于YOLO8、YOLO9和YOLO10等先进模型，YOLO9tr在计算效率与检测精度之间取得平衡，展现出更优的精确度与推理速度。该模型可实现高达136 FPS的帧率，适用于视频监控与自动化巡检系统等实时应用场景。本研究通过消融实验分析架构修改与超参数变化对模型性能的影响，进一步验证了部分注意力模块的有效性。研究结果凸显了YOLO9tr在实际路面状况实时监测中的应用潜力，为开发鲁棒高效的道路基础设施维护解决方案提供了技术支持。