Pixel-Level Crack Identification for Bridge Concrete Structures Using Unmanned Aerial Vehicle Photography and Deep Learning

IF 4.6 2区 工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY
Fei Song, Bo Liu, Guixia Yuan
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引用次数: 0

Abstract

Traditional manual inspection technology has the problems of high risk, low efficiency, and being time-consuming in bridge safety management. The unmanned aerial vehicle (UAV)-based detection technology is widely used in bridge structure safety monitoring. However, the existing deep learning-based concrete crack identification method has great limitations in dealing with complex background and tiny cracks in bridge structures. To address these problems, this study designs a crack pixel-level high-performance segmentation model for bridge concrete cracks that is suitable for UAV detection scenarios using machine vision (MV) and deep learning (DL) algorithms. First, considering the high requirements for the computing performance of the MV-based model for UAV-based detection, the ResNet-18-based lightweight convolutional neural network is used to represent the traditional large-scale backbone network of the pyramid scene parsing network (PSPNet) to develop a high-performance crack automatic identification model. Then, considering that bridge concrete cracks have the characteristics of subtle shapes and complex backgrounds, the spatial position self-attention module is inserted into the PSPNet to improve its detection accuracy. A concrete bridge is used for the case study, and a dataset of cracks in bridge concrete structures collected by UAVs is constructed and used for model training. The experimental results show that the loss function of the developed method in the training process results in a smooth decline, and the developed algorithm achieves the evaluation indicators of 0.9008 precision, 0.8750 recall, 0.8820 accuracy, and 0.9012 IOU on the bridge concrete crack dataset, which are significantly higher than other state-of-the-art baseline methods. In addition, four common UAV bridge detection scenarios, including low light, complex crack forms, high background roughness, and complex background scenes, are used to further test the crack detection ability of the developed crack identification model. The experimental results show that the proposed crack identification method can effectively overcome interference and real-size pixel-level segmentation of crack morphology. In addition, it also achieved a detection efficiency of 35.04 FPS, which shows that the real-time detection ability of the method has good applicability in the UAV detection scene.

Abstract Image

利用无人机摄影和深度学习识别桥梁混凝土结构的像素级裂缝
传统的人工检测技术在桥梁安全管理中存在风险高、效率低、耗时长等问题。基于无人机(UAV)的检测技术在桥梁结构安全监测中得到了广泛应用。然而,现有的基于深度学习的混凝土裂缝识别方法在处理复杂背景和桥梁结构微小裂缝时存在很大局限性。针对这些问题,本研究利用机器视觉(MV)和深度学习(DL)算法设计了一种适用于无人机检测场景的裂缝像素级高性能桥梁混凝土裂缝分割模型。首先,考虑到基于 MV 的无人机检测模型对计算性能要求较高,采用基于 ResNet-18 的轻量级卷积神经网络代表传统的大规模骨干网络金字塔场景解析网络(PSPNet),开发高性能裂缝自动识别模型。然后,考虑到桥梁混凝土裂缝具有形状细微、背景复杂的特点,在 PSPNet 中加入了空间位置自注意模块,以提高其检测精度。以一座混凝土桥梁为例,构建了无人机采集的桥梁混凝土结构裂缝数据集,并用于模型训练。实验结果表明,所开发方法的损失函数在训练过程中会出现平滑下降,所开发算法在桥梁混凝土裂缝数据集上达到了 0.9008 的精度、0.8750 的召回率、0.8820 的准确率和 0.9012 的 IOU 的评价指标,明显高于其他最先进的基线方法。此外,还使用了四种常见的无人机桥梁检测场景,包括弱光、复杂裂缝形态、高背景粗糙度和复杂背景场景,进一步检验了所开发的裂缝识别模型的裂缝检测能力。实验结果表明,所提出的裂缝识别方法能有效克服裂缝形态的干扰和真实尺寸像素级分割。此外,其检测效率也达到了 35.04 FPS,这表明该方法的实时检测能力在无人机检测场景中具有良好的适用性。
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来源期刊
Structural Control & Health Monitoring
Structural Control & Health Monitoring 工程技术-工程:土木
CiteScore
9.50
自引率
13.00%
发文量
234
审稿时长
8 months
期刊介绍: The Journal Structural Control and Health Monitoring encompasses all theoretical and technological aspects of structural control, structural health monitoring theory and smart materials and structures. The journal focuses on aerospace, civil, infrastructure and mechanical engineering applications. Original contributions based on analytical, computational and experimental methods are solicited in three main areas: monitoring, control, and smart materials and structures, covering subjects such as system identification, health monitoring, health diagnostics, multi-functional materials, signal processing, sensor technology, passive, active and semi active control schemes and implementations, shape memory alloys, piezoelectrics and mechatronics. Also of interest are actuator design, dynamic systems, dynamic stability, artificial intelligence tools, data acquisition, wireless communications, measurements, MEMS/NEMS sensors for local damage detection, optical fibre sensors for health monitoring, remote control of monitoring systems, sensor-logger combinations for mobile applications, corrosion sensors, scour indicators and experimental techniques.
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