Measurement最新文献_第4页

DEFSR-Net: A joint learning network of super-resolution enhanced dual-branch edge features for X-ray contraband security detection in energy dispersive spectrometer environment DEFSR-Net：用于能量色散光谱仪环境下x射线违禁品安全检测的超分辨率增强双分支边缘特征联合学习网络

IF 5.6 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Measurement

Pub Date : 2026-05-05 Epub Date: 2026-03-10 DOI: 10.1016/j.measurement.2026.121114

Caohongtai Liu, Tongxin Yan, Yuting Zhang

The expansion of the transportation industry has not only facilitated the flow of people and goods, but also brought security and regulatory challenges. To this end, we propose DEFSR-Net, a novel X-ray contraband detection network with a dual backbone and detection transformer structure. The network integrates image processing and object detection through multi-task joint learning. To enhance the visibility of key features in X-ray images, we adopt a dual-branch structure for collaborative learning. First, we use the Real-ESRGAN super-resolution enhancement dataset and apply a decolorization algorithm to highlight color information. Then, an improved edge enhancement module is used to emphasize edge features and a branched backbone is combined to capture various feature types. Then, we introduce an edge-guided feature fusion module to merge features from different stages of the dual backbone, thereby effectively enhancing multi-scale feature representation and edge receptive field. To address the class imbalance problem, we use Unified-IoU for weight distribution and an annealing strategy to balance training. Extensive experiments on the EDS and CLCXray dataset confirm that DEFSR-Net is suitable for real-time deployment and has high accuracy.

交通运输业的扩张不仅便利了人员和货物的流动，也带来了安全和监管方面的挑战。为此，我们提出了DEFSR-Net，一种新型的双骨干和检测变压器结构的x射线违禁品检测网络。该网络通过多任务联合学习将图像处理和目标检测相结合。为了增强x射线图像关键特征的可见性，我们采用双分支结构进行协同学习。首先，我们使用Real-ESRGAN超分辨率增强数据集，并应用脱色算法突出显示颜色信息。然后，利用改进的边缘增强模块来强调边缘特征，并结合分支主干来捕获各种类型的特征。然后，引入边缘引导特征融合模块，对双主干不同阶段的特征进行融合，从而有效增强多尺度特征表示和边缘接受场；为了解决类不平衡问题，我们使用Unified-IoU进行权重分配，并使用退火策略来平衡训练。在EDS和CLCXray数据集上进行的大量实验证实，DEFSR-Net适合实时部署，具有较高的精度。

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引用次数: 0

Liquid-transformer temporal self-supervised network for few-shot class-incremental fault diagnosis of servo mechanisms 基于液变时间自监督网络的伺服机构小次类增量故障诊断

IF 5.6 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Measurement

Pub Date : 2026-05-05 Epub Date: 2026-03-07 DOI: 10.1016/j.measurement.2026.121078

Zeming Zhang , Chuanyang Li , Changhua Hu , Jianhui Hu , Meng Zhao , Mingzhe Leng , Zhaoqiang Wang , Xinyi Wan , Ziyang Zheng

Electric servo mechanisms are critical actuators in aerospace equipment, where emerging fault types continuously appear under varying operating conditions. Few-shot class-incremental diagnosis is therefore constrained by two coupled challenges: highly nonstationary vibration responses and extremely limited labelled samples, both of which weaken temporal representation learning and aggravate catastrophic forgetting in conventional models. To address these issues, a Liquid-Transformer Temporal Self-Supervised Network (LTTSNet) is proposed for continual recognition of both base and emerging fault classes. Its backbone integrates a Liquid Neural Network (LNN) with a Lightweight Transformer (LTransformer), in which the LNN captures local transient dynamics through learnable time constants, whereas the LTransformer models long-range cross-cycle dependencies. In the base stage, a simple framework for contrastive learning of visual representations is employed to learn invariant representations from scarce unlabelled signals by contrasting augmented views. Pseudo-labels are generated via nearest-neighbour clustering under a cosine-similarity threshold and are jointly trained with labelled samples, thereby introducing latent new-class information before incremental updates. In the incremental stage, attention-weighted prototype estimation and one-step gradient prototype distillation are jointly employed to refine new-class prototypes. The backbone is kept frozen, and only the classifier head is updated, enabling rapid adaptation to new classes while preserving old-class discrimination. Experiments on a laboratory electric servo mechanism fault dataset and the Case Western Reserve University bearing dataset demonstrate that LTTSNet delivers significantly improves overall accuracy, new-class recognition, and forgetting suppression under cross-condition few-shot settings with both single and compound faults.

电动伺服机构是航空航天设备的关键执行机构，在不同的运行条件下不断出现新的故障类型。因此，少数次类增量诊断受到两个耦合挑战的限制：高度非平稳的振动响应和极其有限的标记样本，这两者都削弱了传统模型中的时间表征学习并加剧了灾难性遗忘。为了解决这些问题，提出了一种液变短时自监督网络（LTTSNet）来连续识别基本故障和新故障。它的主干集成了液体神经网络（LNN）和轻型变压器（LTransformer），其中LNN通过可学习的时间常数捕获局部瞬态动态，而LTransformer则建模长期交叉周期依赖关系。在基础阶段，采用一个简单的视觉表征对比学习框架，通过对比增强视图从稀缺的未标记信号中学习不变表征。伪标签通过余弦相似阈值下的最近邻聚类生成，并与标记样本联合训练，从而在增量更新之前引入潜在的新类别信息。在增量阶段，采用注意力加权原型估计和一步梯度原型蒸馏相结合的方法对新类别原型进行细化。骨干保持冻结，只有分类器头部更新，能够快速适应新类别，同时保留旧类别的区分。在实验室电动伺服机构故障数据集和凯斯西储大学轴承数据集上的实验表明，LTTSNet在单故障和复合故障的交叉条件少射设置下，显著提高了整体准确率、新类别识别和遗忘抑制。

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引用次数: 0

Real-time sequence components extraction for an unbalanced distribution system for fault detection 不平衡配电系统故障检测的实时序列分量提取

IF 5.6 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Measurement

Pub Date : 2026-05-05 Epub Date: 2026-03-06 DOI: 10.1016/j.measurement.2026.121048

Sushil Karvekar, Jayesh Kharat, Harshwardhan Khot, Prathmesh Gadkari

The objective of this study is to extract sequence components in a distribution system utilizing hardware-in-loop (HIL) for accurate fault detection and classification. The proposed system focuses on real-time monitoring using fixed-point arithmetic on a low-cost TMS320F28379D launch-xl DSP microcontroller, enabling quick computation and fault detection based on the sequence parameters. This study proposes a Fourier-based modified extraction technique for a three-phase induction motor that serves as a prototype of a distribution system. The real-time sequence component extraction of the voltage and current signals in the transmission lines was carried out using a Fourier-based extraction technique. The amplitude and phase of all the sequence components were successfully extracted with variable sampling frequencies. The system performance was successfully tested for both symmetrical and unsymmetrical faults in transmission system within three to five power cycles, as per the IEEE C37.103 standard for overcurrent protection in transmission lines. This research effectively illustrated real-time sequence extraction, enabling a rapid reaction to imbalances in the system. Reliable sequence component extraction is made possible by the use of HIL, which makes it easier to track changes in real time. Optimization of the Fourier-based extraction algorithm improves the overall execution speed and reduces the computational burden and memory utilization of the DSP. The algorithm can be deployed on low-cost target DSP platforms for HIL testing. Furthermore, the system is easily scalable and adaptable, with minimal changes to meet the requirements of changing physical conditions.

本研究的目的是利用硬件在环（HIL）提取配电系统中的序列分量，以进行准确的故障检测和分类。该系统的重点是在低成本的TMS320F28379D发射-xl DSP微控制器上采用定点算法实现实时监控，实现基于序列参数的快速计算和故障检测。本研究提出了一种基于傅里叶的改进提取技术，用于作为配电系统原型的三相感应电动机。采用基于傅里叶的提取技术对输电线路中电压和电流信号进行实时序列分量提取。在可变采样频率下，成功地提取了所有序列分量的幅值和相位。按照IEEE C37.103输电线路过流保护标准，在3 ~ 5个功率周期内，成功测试了输电系统的对称和非对称故障。该研究有效地说明了实时序列提取，能够对系统中的不平衡做出快速反应。通过使用HIL，可靠的序列成分提取成为可能，这使得实时跟踪变化变得更加容易。对基于傅里叶的提取算法进行优化，提高了总体执行速度，降低了DSP的计算量和内存利用率。该算法可以部署在低成本的目标DSP平台上进行HIL测试。此外，该系统易于扩展和适应，只需最小的更改即可满足不断变化的物理条件的要求。

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引用次数: 0

Noncontact inversion method for anchored rock bolt axial load based on deep vision–deep learning fusion 基于深度视觉深度学习融合的锚杆轴向载荷非接触反演方法

IF 5.6 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Measurement

Pub Date : 2026-05-05 Epub Date: 2026-03-06 DOI: 10.1016/j.measurement.2026.121065

Zhengxiang He , Xingliang Xu , Pingan Peng , Liguan Wang , Suchuan Tian

The axial load of rock bolts serves as a vital indicator for monitoring this mechanical state. However, existing axial load measurement methods rely primarily on installing sensors on bolts, resulting in limited monitoring approaches, high costs, and poor feedback timeliness. Therefore, this paper proposes a noncontact load inversion method for rock bolts based on the integration of deep vision and deep learning. Innovatively, we establish a constitutive model that links the surface deformation of anchor plates to the axial load of bolts through deep learning. By incorporating a multiscale patch embedding block and a gated residual attention mechanism, we enhance the Vision Transformer (ViT) model, developing a multiscale gated vision transformer for load inversion computation. The proposed method was validated through laboratory experiments and field tests. In laboratory, it achieved a coefficient of determination (R²) of 0.97 for axial load prediction, outperforming the Gated Transformer (0.94), ViT (0.95), ResNet (0.92), and CNN (0.95). During the field tests, the model attained an R² value of 0.96. Additionally, we analyzed the impact of the measurement offset at the anchor plate on the axial load inversion accuracy. The results demonstrate that the proposed noncontact method efficiently inverts the axial load of rock bolts.

锚杆轴向载荷是监测锚杆力学状态的重要指标。然而，现有的轴向载荷测量方法主要依赖于在螺栓上安装传感器，导致监测方法有限，成本高，反馈及时性差。因此，本文提出了一种基于深度视觉和深度学习相结合的锚杆非接触载荷反演方法。创新地，我们通过深度学习建立了锚板表面变形与螺栓轴向载荷之间的本构模型。结合多尺度贴片嵌入块和门控剩余注意机制，对视觉变压器（ViT）模型进行了改进，开发了一种用于负载反演计算的多尺度门控视觉变压器。通过室内试验和现场试验验证了该方法的有效性。在实验室中，其轴向负荷预测的决定系数（R2）为0.97，优于门控变压器（0.94）、ViT（0.95）、ResNet（0.92）和CNN（0.95）。在现场试验中，该模型的R2值为0.96。此外，我们还分析了锚板处测量偏移对轴向载荷反演精度的影响。结果表明，所提出的非接触方法能有效地反演锚杆轴向荷载。

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引用次数: 0

Miniaturized high-resolution Fourier ptychographic microscopy based on fiber optic array 基于光纤阵列的小型化高分辨率傅立叶平面显微镜

IF 5.6 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Measurement

Pub Date : 2026-05-05 Epub Date: 2026-03-09 DOI: 10.1016/j.measurement.2026.121073

Weiming Wang , Ruixue Li , Xinjie Zhang , Haoru Xin , Yangjian Cai

Fourier ptychographic microscopy (FPM) is a computational imaging method that reconstructs high-resolution images by synthesizing multiple low-resolution images captured under varying illumination angles. In this work, we propose a miniaturized FPM based on the fiber optic array (FOA, 6×,

NA = 0.65

) which can improve the resolution from 9.84 μm to 1.74 μm compared to conventional objective (10×,

NA = 0.25

)-based FPM. The FOA enables distortion-free microscopy imaging, while a programmable LED array provides the angular illumination required for FPM recovery and synthetic aperture generation. The system couples the FOA to the smartphone’s built-in camera for microscopy imaging. Furthermore, by employing a hybrid FOA-Lens FPM (FOA, lens and FPM, termed FLFPM), the resolution is enhanced to 775 nm. Within a consistent field of view of 92 μm × 185 μm, there is no visible naked-eye distortion. Line-pair grayscale profile analysis was performed on all resolution images to provide additional evidence that the selected line pairs are distinguishable. Moreover, we investigated the resolution performance of various configurations, including the FOA-only mode, the FOA-FPM mode, and the FLFPM. To quantify performance, we compared the signal-to-background ratio (SBR) of these systems. The FLFPM demonstrated the highest contrast, indicating superior background suppression and signal extraction. This advantage was further confirmed by Modulation Transfer Function (MTF) analysis, where the FLFPM achieved the highest spatial frequency (592.90 lp/mm) at a 0.7 contrast threshold. This method provides a hardware improvement approach for enhancing the resolution of FPM.

傅里叶显微成像（FPM）是一种通过合成在不同照明角度下捕获的多幅低分辨率图像来重建高分辨率图像的计算成像方法。在这项工作中，我们提出了一种基于光纤阵列（FOA, 6×， NA=0.65）的小型化FPM，与基于传统物镜（10×， NA=0.25）的FPM相比，它可以将分辨率从9.84 μm提高到1.74 μm。FOA可以实现无畸变显微镜成像，而可编程LED阵列提供FPM恢复和合成孔径生成所需的角度照明。该系统将FOA与智能手机的内置摄像头相结合，用于显微镜成像。此外，通过采用混合FOA- lens FPM (FOA， lens和FPM，称为FLFPM)，分辨率提高到775 nm。在92 μm × 185 μm的一致视场内，没有可见的肉眼畸变。对所有分辨率图像进行线对灰度轮廓分析，以提供所选线对可区分的额外证据。此外，我们还研究了各种配置的分辨率性能，包括foa -纯模式、FOA-FPM模式和FLFPM模式。为了量化性能，我们比较了这些系统的信背景比（SBR）。FLFPM具有最高的对比度，表明具有良好的背景抑制和信号提取能力。调制传递函数（MTF）分析进一步证实了这一优势，在0.7对比度阈值下，FLFPM达到了最高的空间频率（592.90 lp/mm）。该方法为提高FPM分辨率提供了一种硬件改进途径。

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引用次数: 0

Semi-MCDA-Net: A novel class-specific cross-domain fault diagnosis technique under time-varying speed conditions with limited labeled data Semi-MCDA-Net：一种基于有限标记数据的时变速度条件下的跨域故障诊断技术

IF 5.6 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Measurement

Pub Date : 2026-05-05 Epub Date: 2026-03-08 DOI: 10.1016/j.measurement.2026.121072

Misbah Iqbal , C.K.M. Lee , J.Z. Ren , Xingchen Liu

Time-varying rotational speed conditions cause domain shifts in rotating machines, resulting in discrepancies between training and testing datasets, preventing transfer learning models operating at constant speed conditions from detecting invariant features and reducing their generalization efficacy. Moreover, the current transfer learning methods for varying speed scenarios mainly focus on aligning the marginal data distribution while neglecting the influence of class-specific feature alignment on the diagnostic accuracy. To address these limitations, this research develops a novel end-to-end semi-supervised marginal and conditional distribution alignment network (Semi-MCDA-Net) to deal with the issues of fault diagnosis under variable speed working conditions, particularly in the context of insufficient labeled data in the target domain. Our method integrates multi-kernel Maximum Mean Discrepancy (MMD) and Wasserstein distance (WD) to develop a unified domain alignment module, systematically applied across multiple convolutional layers of a shared 1D-CNN. In contrast to conventional transfer learning approaches, the proposed methodology effectively acquires features that are both domain-invariant and class-discriminative, explicitly aligns both marginal and conditional distributions, and thereby generalizes to data in the target domain while improving accuracy near the class decision boundaries. Two case studies are carried out to verify the efficacy and generalizability of Semi-MCDA-Net method.

时变的转速条件会引起旋转机器的域移位，导致训练数据集和测试数据集之间的差异，阻碍了在恒转速条件下运行的迁移学习模型检测不变性特征，降低了其泛化效率。此外，目前针对变速场景的迁移学习方法主要关注边缘数据分布的对齐，而忽略了特定类别特征对齐对诊断准确率的影响。为了解决这些限制，本研究开发了一种新颖的端到端半监督边缘和条件分布对齐网络（Semi-MCDA-Net）来处理变速工况下的故障诊断问题，特别是在目标域标记数据不足的情况下。我们的方法集成了多核最大平均差异（MMD）和沃瑟斯坦距离（WD），开发了一个统一的域对齐模块，系统地应用于共享1D-CNN的多个卷积层。与传统的迁移学习方法相比，该方法有效地获取了领域不变和类别区分的特征，明确地对齐边缘分布和条件分布，从而推广到目标领域的数据，同时提高了类决策边界附近的准确性。通过两个实例验证了Semi-MCDA-Net方法的有效性和通用性。

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引用次数: 0

A hierarchical and progressive ship-feature-based method for berthing and unberthing situation awareness 一种基于船舶特征的分层递进靠泊态势感知方法

IF 5.6 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Measurement

Pub Date : 2026-05-05 Epub Date: 2026-03-04 DOI: 10.1016/j.measurement.2026.121035

Tian-Qi Wang, Ying Li

To ensure the safety of the ship and port, it is crucial to accurately perceive and analyze the ship’s motion behavior and spatiotemporal features during the berthing and unberthing process. This paper proposes a ship-feature-based hierarchical and progressive situation awareness method. In this method, a progressive perception framework, progressing layer by layer from the data layer to the behavioral layer and then to the global layer, is constructed. Firstly, the Kalman filter algorithm is employed to measurement key parameters during the berthing and unberthing process, thereby obtaining the ship’s motion information. Secondly, by integrating the social force model with the motion information, the interaction forces between the ship and the shoreline are calculated, and then the behavior features of the ship are identified. Thirdly, a dynamic buffer zone is constructed in the spatiotemporal domain, and an adaptive smoothing method is introduced to extract key time points and evolutionary patterns, thereby enabling comprehensive spatiotemporal modeling of the entire berthing and unberthing process. At last, the feasibility of the proposed method is verified through the berthing and unberthing experiments conducted in Dalian Port, China.

为了保证船舶和港口的安全，准确地感知和分析船舶在靠泊和离泊过程中的运动行为和时空特征至关重要。提出了一种基于舰船特征的分层递进态势感知方法。该方法构建了一个从数据层到行为层再到全局层逐层递进的感知框架。首先，利用卡尔曼滤波算法对船舶靠泊和离泊过程中的关键参数进行测量，从而获得船舶的运动信息；其次，将社会力模型与运动信息相结合，计算船舶与岸线之间的相互作用力，识别船舶的行为特征；第三，在时空域构建动态缓冲区，引入自适应平滑方法提取关键时间点和演化模式，实现对整个靠泊和离泊过程的全面时空建模；最后，通过在中国大连港进行的靠泊和离泊实验，验证了所提方法的可行性。

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引用次数: 0

Cutoff frequency determination algorithm for ferroelectric device pulse measurements with application to machine learning based prediction 铁电器件脉冲测量截止频率确定算法及其在机器学习预测中的应用

IF 5.6 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Measurement

Pub Date : 2026-05-05 Epub Date: 2026-03-05 DOI: 10.1016/j.measurement.2026.120959

Seungyong Byun , Jinwoo Choi , Hyunwoo Park

The accurate characterization of the ferroelectric switching dynamics is essential but challenging. Square-pulse measurements are commonly used, yet achieving both an accurate and precise level of measurement leads to weak current responses and poor signal-to-noise ratios. Since polarization is obtained by time-integrating these noisy currents, reliable denoising is critical. However, typical Conventional fast Fourier transform (FFT)-based noise filtering relies on manual determination by a person of the cutoff frequency in the Fourier domain, without a rigorous or reproducible criterion, which makes it difficult to automate the process. To address this issue, we propose a cutoff frequency determination algorithm that finds the frequency at which the inverse gradient of the logarithmic mean squared error (log MSE) curve is minimized. Furthermore, we developed a machine learning model combining 1-D dilated convolution and gated recurrent unit (GRU) layers to predict the cutoff frequency labeled by the proposed algorithm. This model performed particularly well in cases with a limited number of data points, where the algorithm tended to fail, effectively capturing both local and global patterns in signals. The post-processing following noise filtering with the determined cutoff frequency was implemented as well, enabling efficient handling of large measurement datasets.

准确表征铁电开关动力学是必要的，但具有挑战性。平方脉冲测量是常用的，然而，实现精确和精确的测量水平导致弱电流响应和差的信噪比。由于极化是通过对这些噪声电流进行时间积分得到的，因此可靠的去噪至关重要。然而，典型的基于快速傅里叶变换（FFT）的传统噪声滤波依赖于人工确定傅里叶域中的截止频率，没有严格或可重复的准则，这使得该过程难以自动化。为了解决这个问题，我们提出了一种截止频率确定算法，该算法可以找到对数均方误差（log MSE）曲线的逆梯度最小的频率。此外，我们开发了一个结合一维扩展卷积和门控循环单元（GRU）层的机器学习模型，以预测所提出算法标记的截止频率。这个模型在数据点数量有限的情况下表现得特别好，在这种情况下算法往往会失败，有效地捕获信号中的局部和全局模式。采用确定的截止频率进行噪声滤波后的后处理，实现对大型测量数据集的高效处理。

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引用次数: 0

Frequency-Resolved Measurement of Power losses in Miniature Circuit Breakers (MCBs) under harmonic current 谐波电流下微型断路器功率损耗的分频测量

IF 5.6 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Measurement

Pub Date : 2026-05-05 Epub Date: 2026-02-28 DOI: 10.1016/j.measurement.2026.120994

Łukasz Drużyński , Grzegorz Dombek , Andrzej Książkiewicz

This paper investigates the impact of current waveform distortion on apparent power losses in miniature circuit breakers (MCBs). The increasing penetration of nonlinear loads in low-voltage installations results in current waveforms with significant harmonic content, which may substantially affect the electrical and thermal behaviour of protective devices. An experimental methodology for frequency-resolved assessment of power losses in MCB current paths is presented, based on synchronized measurements of RMS voltage drop and RMS current under controlled harmonic excitation. Measurements were performed on MCBs with different rated currents, subjected to individual current harmonics up to the 25th order (1250 Hz) and to composite distorted waveforms representative of industrial and office installations. The results show a clear frequency-dependent increase in apparent power losses. Depending on the breaker rating, the measured losses increase by approximately 58–64% when comparing operation at the fundamental frequency (50 Hz) with higher-order harmonics within the investigated range. Devices with higher rated currents exhibit a steeper growth of losses with increasing harmonic order. The obtained results indicate that harmonic-rich currents significantly increase the thermal loading of MCB current paths, even when the RMS current value is maintained at a constant level. The study emphasizes the importance of accounting for frequency-dependent impedance effects and waveform distortion when evaluating power losses and thermal performance of miniature circuit breakers in modern low-voltage power systems.

本文研究了电流波形畸变对微型断路器视在功率损耗的影响。在低压装置中，非线性负载的渗透增加导致电流波形具有显著的谐波含量，这可能会严重影响保护装置的电气和热行为。提出了一种基于同步测量可控谐波激励下的有效值压降和有效值电流的频率分辨MCB电流路径功率损耗的实验方法。在不同额定电流的微型断路器上进行测量，承受高达25阶（1250 Hz）的单个电流谐波和代表工业和办公装置的复合畸变波形。结果表明，视在功率损耗明显随频率增加。根据断路器额定值的不同，当将基频（50 Hz）与研究范围内的高次谐波进行比较时，测量到的损耗增加了大约58-64%。具有较高额定电流的器件，其损耗随谐波阶数的增加而急剧增长。结果表明，即使均方根电流值保持在一定水平，富谐波电流也会显著增加MCB电流路径的热负荷。该研究强调了在评估现代低压电力系统中微型断路器的功率损耗和热性能时，考虑频率相关阻抗效应和波形畸变的重要性。

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引用次数: 0

An image-guide–PCD modal fusion method for concrete defects spatial feature understanding 基于图像引导- pcd模态融合的混凝土缺陷空间特征识别方法

IF 5.6 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Measurement

Pub Date : 2026-05-05 Epub Date: 2026-03-09 DOI: 10.1016/j.measurement.2026.121105

Yanjie Zhu , Xiangteng Ma , Wen Xiong

Cracks inevitably develop in concrete structures during service, facilitating the ingress of water and chlorides, which accelerates steel corrosion and deterioration. Compared with 2D metrics, 3D indicators including crack depth and volume are crucial for determining whether cracks penetrate the protective cover, assessing material loss, and defining repair scope and risk levels. However, commonly used image based inspection methods struggle to provide 3D information of defects. To address this issue, this paper leverages the complementary strengths of 3D point cloud data (PCD) and 2D images and proposes an image-guide–PCD modal fusion method to enable precise defect detection and spatial feature quantitative characterization. First, an image-guided cross modal registration algorithm is developed to align 2D image with 3D PCD of the monitored concrete target. Then, BrC-UNet is proposed to segment defects from image with high-accuracy defect masks, which is further guided defects segmented from PCD data. Furthermore, a 3D defect quantification framework is developed to characterize crack depth, length, width, and volume. Experiments show cross modal registration errors around 2% and image-guided PCD segmentation mIoU of 0.833. The mean quantification errors for defects’ depth, length, and maximum width are 4.87%, 4.29%, and 6.04%, and volume estimation outperforms conventional methods. The proposed extraction and quantification framework transcends 2D limitations, enabling multidimensional analysis for structural evaluation and durability prediction.

混凝土结构在使用过程中不可避免地出现裂缝，使水和氯化物进入，加速了钢的腐蚀和劣化。与2D指标相比，包括裂缝深度和体积在内的3D指标对于确定裂缝是否穿透防护罩、评估材料损失、确定修复范围和风险水平至关重要。然而，常用的基于图像的检测方法难以提供缺陷的三维信息。为了解决这一问题，本文利用三维点云数据（PCD）和二维图像的互补优势，提出了一种图像引导- PCD模态融合方法，实现了精确的缺陷检测和空间特征定量表征。首先，开发了一种图像引导的交叉模态配准算法，将二维图像与被监测具体目标的三维PCD对齐。然后，提出BrC-UNet算法，利用高精度的缺陷掩模从图像中分割缺陷，进而指导从PCD数据中分割缺陷。此外，开发了一个三维缺陷量化框架来表征裂纹深度、长度、宽度和体积。实验表明，交叉模态配准误差约为2%，图像引导的PCD分割mIoU为0.833。缺陷深度、长度和最大宽度的平均量化误差分别为4.87%、4.29%和6.04%，体积估计优于传统方法。提出的提取和量化框架超越了二维限制，使结构评估和耐久性预测的多维分析成为可能。

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