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YOLO9tr: A Lightweight Model for Pavement Damage Detection Utilizing a Generalized Efficient Layer Aggregation Network and Attention Mechanism

2024-06-18 09:30:21
Sompote Youwai, Achitaphon Chaiyaphat, Pawarotorn Chaipetch


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.

Abstract (translated)

维护道路沥青路面完整性对确保安全和高效的运输至关重要。通常评估沥青路面状况的方法往往费力且容易出错。本文提出了一种名为YOLO9tr的新型轻量级沥青路面损伤检测模型,利用深度学习的进展。YOLO9tr基于YOLOv9架构,包括一个部分注意块,可以增强特征提取和关注机制,从而在复杂场景下提高检测性能。该模型在包括多个国家的道路损坏图像的全面数据集上进行训练,包括扩展的损坏类别。这种扩展的分类范围使得对道路状况的评估更加准确和现实。比较分析显示,YOLO9tr相对于最先进的YOLO8、YOLO9和YOLO10模型具有更卓越的精度和推理速度,实现计算效率与检测准确性的平衡。该模型达到高达136 FPS的高帧率,使其适用于实时应用,如视频监控和自动检测系统。研究还进行了一项消融研究,分析建筑修改和超参数变化对模型性能的影响,进一步验证了部分注意块的有效性。结果表明,YOLO9tr在实时道路沥青路面状况监测方面具有潜在应用价值,为保持安全、功能完备的道路基础设施发展做出了贡献。



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