基于改进的 YOLOv5s 模型的探地雷达图像中的道路地下缺陷探测

IF 2.2 3区 地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY
Wei Xue , Ting Li , Jiao Peng , Li Liu , Jian Zhang
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引用次数: 0

摘要

道路地下缺陷探测在评估交通基础设施方面发挥着至关重要的作用。为此,地面穿透雷达(GPR)是一种广泛使用的地球物理工具。然而,传统的 GPR 图像人工判读严重依赖从业人员的经验,导致效率低下和误差较大。针对这些挑战,本文提出了一种基于改进的 YOLOv5s 模型的道路地下缺陷自动检测方法。首先,将密集连接结构集成到主干网的 C3 模块中,形成 Dense-C3 模块,以增强特征提取能力。随后,在每个 Dense-C3 模块之后加入卷积块注意模块(CBAM),以完善特征并提高效率。此外,置信度损失采用了焦点损失函数,以减轻样本不平衡对检测性能的影响。实验结果表明,所提出的模型对合成数据的平均精度(mAP)为 96.4%,对真实数据的平均精度(mAP)为 91.9%,优于其他七个模型。所提模型对真实数据的检测速度达到每秒 51 帧,满足了道路地下缺陷的实时检测要求。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Road underground defect detection in ground penetrating radar images based on an improved YOLOv5s model

Road underground defect detection plays a crucial role in assessing transportation infrastructure. Ground penetrating radar (GPR) serves as a widely used geophysical tool for this purpose. However, the traditional manual interpretation of GPR images heavily relies on the experience of the practitioner, leading to inefficiency and inaccuracies. To tackle these challenges, this paper proposes an automatic detection method for underground defects of roads based on an improved YOLOv5s model. First, the dense connection structure is integrated in the C3 module of the backbone to form the Dense-C3 module to enhance the capability of feature extraction. Subsequently, a convolutional block attention module (CBAM) is incorporated after each Dense-C3 module to refine features and enhance efficiency. Furthermore, the focal loss function is employed for the confidence loss to mitigate the impact of sample imbalance on detection performance. Experimental results demonstrate that the proposed model achieves a mean average precision (mAP) of 96.4% for synthetic data and 91.9% for real data, outperforming seven other models. The detection speed of the proposed model for real data reaches 51 frames per second, meeting the real-time detection requirements of road underground defects.

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来源期刊
Journal of Applied Geophysics
Journal of Applied Geophysics 地学-地球科学综合
CiteScore
3.60
自引率
10.00%
发文量
274
审稿时长
4 months
期刊介绍: The Journal of Applied Geophysics with its key objective of responding to pertinent and timely needs, places particular emphasis on methodological developments and innovative applications of geophysical techniques for addressing environmental, engineering, and hydrological problems. Related topical research in exploration geophysics and in soil and rock physics is also covered by the Journal of Applied Geophysics.
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