YOLO9tr:利用广义高效层聚合网络和关注机制进行路面损坏检测的轻量级模型

IF 2.9 4区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Sompote Youwai, Achitaphon Chaiyaphat, Pawarotorn Chaipetch
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引用次数: 0

摘要

保持路面的完整性对于确保安全高效的运输至关重要。评估路面状况的传统方法往往费力且容易出现人为错误。本文提出的 YOLO9tr 是一种新颖的轻量级物体检测模型,利用深度学习的先进技术进行路面损坏检测。YOLO9tr 基于 YOLOv9 架构,结合了部分注意力模块,增强了特征提取和注意力机制,从而提高了复杂场景下的检测性能。该模型在由多个国家的道路损坏图像组成的综合数据集上进行了训练。除标准的四种类型(纵向裂缝、横向裂缝、鳄鱼裂缝和坑洞)外,该数据集还包括一组扩展的损坏类别,可对道路损坏进行更细致的分类。分类范围的扩大使得对路面状况的评估更加准确和真实。对比分析表明,与 YOLOv8、YOLOv9 和 YOLOv10 等最先进的模型相比,YOLO9tr 具有更高的精度和推理速度,实现了计算效率和检测精度之间的平衡。该模型的帧速率高达 136 FPS,适合视频监控和自动检测系统等实时应用。研究介绍了一项消融研究,分析了架构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力区块的有效性。研究结果凸显了 YOLO9tr 在路面状况实时监控领域的实际应用潜力,有助于开发稳健高效的解决方案,维护道路基础设施的安全和功能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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

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.

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来源期刊
Journal of Real-Time Image Processing
Journal of Real-Time Image Processing COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-ENGINEERING, ELECTRICAL & ELECTRONIC
CiteScore
6.80
自引率
6.70%
发文量
68
审稿时长
6 months
期刊介绍: Due to rapid advancements in integrated circuit technology, the rich theoretical results that have been developed by the image and video processing research community are now being increasingly applied in practical systems to solve real-world image and video processing problems. Such systems involve constraints placed not only on their size, cost, and power consumption, but also on the timeliness of the image data processed. Examples of such systems are mobile phones, digital still/video/cell-phone cameras, portable media players, personal digital assistants, high-definition television, video surveillance systems, industrial visual inspection systems, medical imaging devices, vision-guided autonomous robots, spectral imaging systems, and many other real-time embedded systems. In these real-time systems, strict timing requirements demand that results are available within a certain interval of time as imposed by the application. It is often the case that an image processing algorithm is developed and proven theoretically sound, presumably with a specific application in mind, but its practical applications and the detailed steps, methodology, and trade-off analysis required to achieve its real-time performance are not fully explored, leaving these critical and usually non-trivial issues for those wishing to employ the algorithm in a real-time system. The Journal of Real-Time Image Processing is intended to bridge the gap between the theory and practice of image processing, serving the greater community of researchers, practicing engineers, and industrial professionals who deal with designing, implementing or utilizing image processing systems which must satisfy real-time design constraints.
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