This paper introduces a novel approach for 3D Electromagnetic Tomography (EMT), designing a 3D EMT measurement system for volumetric conductivity reconstruction and defect detection, enabled by an innovative hybrid that combines a sensitivity-based algorithm with a Transformer–UNet model. Traditional non-iterative algorithms such as linear back projection (LBP), Tikhonov Regularization (TR), and Singular Value Decomposition (SVD) face limitations such as reduced accuracy in complex scenarios and susceptibility to noise. To overcome these issues, we propose a TR algorithm enhanced by bi-Laplacian regularization, significantly improving reconstruction quality. Extensive simulations and experimental validations demonstrate that the proposed method outperforms conventional algorithms, achieving higher quantitative gains. Furthermore, integrating deep learning techniques, specifically the proposed method, enables precise reconstruction by effectively combining raw electromagnetic signals with the prior reconstructed results based on the proposed reconstructed algorithm, thereby significantly improving robustness, accuracy, and detail preservation. Experimental verification confirms the practical efficacy and robustness of this hybrid model for industrial applications. This framework differs from prior purely data-driven reconstructions by explicitly coupling a sensitivity-based EMT solver with a Transformer–UNet trained with a supervised image-domain loss, where a physics-based pre-reconstruction is provided as an additional prior, enabling real-time monitoring with offline shape-accurate refinement. • A 3D EMT framework is proposed for conductivity and contour reconstruction. • A triple-regularized scheme improves volumetric imaging and defect depiction. • A dual-branch network enhances reconstruction accuracy and robustness.

Lightweight fault diagnosis (FD) models are essential for efficient inference on resource-constrained IoT edge devices and practical industrial deployment. While existing lightweight FD research has achieved notable progress in network architecture compression, the substantial benefits brought by quantization techniques remain largely overlooked. However, directly applying mainstream quantization methods to FD tasks is problematic, as they suffer from two main drawbacks: 1) Uniform quantization struggles to simultaneously maintain local resolution in dense regions and dynamic range coverage in sparse regions. 2) Mainstream quantization-aware training (QAT) methods rely on the straight-through estimator (STE) to approximate discrete gradients, introducing gradient bias that further limits model accuracy improvement. To address these issues, this paper proposes an Adaptive Quantization Error Minimization Method (AQEMM) for fault diagnosis, with the following main contributions: 1) An adaptive quantizer is introduced to replace the traditional fixed quantization step size, expanding the search space of quantization points from discrete integer grids to a continuous subspace, enabling quantization levels to adaptively concentrate in data-dense regions. 2) A block coordinate descent strategy is incorporated to alternately optimize quantization parameters, with quantization reconstruction error minimization as the direct optimization objective, fundamentally circumventing the gradient bias introduced by STE. Validation on FD tasks for two typical industrial devices, induction motors (IM) and permanent magnet synchronous motors (PMSM), demonstrates that the proposed 2-bit quantization model saves up to approximately 82% of hardware resources compared to full-precision (FP) deployment on an FPGA platform while maintaining competitive diagnostic accuracy. Furthermore, by optimizing the hardware design to leverage the released hardware resource margin, a significant inference speedup of approximately 20.6× is achieved over pure software inference.

Autonomous driving (AD) systems requires multisensor collaboration to address challenges caused by complex weather scenarios. The recognition accuracy of autonomous driving system based on single resource, either on image only or Lidar only, becomes unreliable due to untested weather conditions, occlusions objects, and other factors. This paper proposes a decision-level fusion network based on an improved YOLOV7 and an improved CenterPoint network to build a multi-sensor-collaboration scheme. The overall accuracy of the proposed fusion algorithm is improved for small targets by adding multi-scale and multi-stage attention channel modules into the backbones of image recognition network and point cloud recognition network respectively. Moreover, the fusion algorithm introduces mixed distance constraints as the loss function for overlapping targets. The proposed fusion algorithm has been successfully tested on the public ONCE dataset mixed with a self-built dataset under various road conditions such as sunny, night, and rainy weather. The mAP of proposed decision-level fusion algorithm achieves$\mathbf{8 3. 5 \%}$in sunny daytime,$\mathbf{8 0 \%}$during nighttime and 79.1 % during rain time.

Semantic segmentation is a key technique that enables mobile robots to understand and navigate surrounding environments autonomously. However, most existing works focus on segmenting known objects, overlooking the identification of unknown classes, which is common in real-world applications. In this paper, we propose a feature-oriented framework for open-set semantic segmentation on LiDAR data, capable of identifying unknown objects while retaining the ability to classify known ones. We design a decomposed dual-decoder network to simultaneously perform closed-set semantic segmentation and generate distinctive features for unknown objects. The network is trained with multi-objective loss functions to capture the characteristics of known and unknown objects. Using the extracted features, we introduce an anomaly detection mechanism to identify unknown objects. By integrating the results of close-set semantic segmentation and anomaly detection, we achieve effective feature-driven LiDAR open-set semantic segmentation. Evaluations on both SemanticKITTI and nuScenes datasets demonstrate that our proposed framework significantly outperforms state-of-the-art methods. The source code will be made publicly available at https://github.com/nubot-nudt/DOSS.

Parameterized Quantum Circuits (PQCs) with fixed structures severely degrade the performance of Quantum Machine Learning (QML). To address this, a Hybrid Quantum-Classical Classifier (HQCC) is proposed. It adaptively optimizes the PQC through a dynamic circuit generator driven by Long Short Term Memory (LSTM) and exploits architectural plasticity to balance the entanglement performance and expressiveness, opening up a practical pathway for efficiently deploying QML in the Noisy Intermediate-Scale Quantum (NISQ) era. Extensive experiments on MNIST and Fashion-MNIST demonstrate that HQCC achieves up to 99.86% accuracy in binary classification and 97.12% in multi-class tasks, surpassing state-of-the-art quantum and classical baselines.

Scene perception for autonomous vehicles and vessels is crucial for autonomous navigation. Current mainstream transformer methods typically split the feature map into windows, such as local, dilated, and horizontal/vertical bar windows. However, their token interaction is confined to fixed windows, posing challenges for image-based semantic segmentation. This article proposes a novel model, LBFormer, which enables flexible token interaction across different windows. Specifically, a window-level affinity graph is constructed from coarse-grained features using self-attention clustering and evolves during training, retaining top-k windows with high semantic relevance for each window. Self-attention purification is then employed to compress and filter fine-grained features with low semantic relevance within the top-k windows, ensuring effective token interaction for each feature point. To enhance context modeling within windows and build a more effective window-level affinity graph, a dual branch method extracts multidimensional features from each window, which are then interacted with and fused via the feature aggregation module. Extensive experiments at an image resolution of 224×224 were conducted on our private YZ-DATA water surface scene dataset and the public CamVid urban scene dataset. The results show that LBFormer achieves an MIoU of 89.80% on YZ-DATA and 61.31% on CamVid, surpassing mainstream transformer methods.

This article proposes a novel 3-D surface temperature prediction model based on the restored pseudoheat flux (RPHF) theory. The method can be used to simulate the temperature difference between the subsurface defect and the sound area. The proposed model shows the potential to investigate the detection limits associated with the defect features, such as depth, radius, diameter-to-depth ratio (D2dR), and excitation features, which is beneficial for the experimental design. Several experiments were conducted on specimens of different materials [glass fiber reinforced plastic (GFRP), CFRP, and rubber] using RPHF thermography to validate the practicality of the model. The comparative analysis is also conducted with other methods. Both experimental and simulation results demonstrated that longer heating is required for deeper defects and the moment of maximum temperature difference tends to appear after the heating has stopped. Probability of detection (PoD) was used as an index to assess the reliability of the methodology and the problems found. The depth of the defect has a greater influence on thermal detection than D2dR.

In this study, we propose a new post processing algorithm, using a stable low-rank decomposed pseudo restored heat flux based on the ensemble variational Bayes tensor factorization (EVBTF-RPHF) algorithm for performing periodic square wave thermographic nondestructive testing (thermographic NDT). Previous studies have shown that both RPHF and EVBTF can separately improve the detectability of thermography by enhancing some defect features. However, both methods are limited by their particularly constraints: RPHF are heavily degraded by noises and missing data due to the assumptions under which the physical models are derived while efficiency of EVBT reduces when the lateral heat diffusion weights out. By embedding RPHF into the stable low-rank decomposition EVBTF, the proposed algorithm allows to improve the detectability of defects in thermographic NDT using a periodic heat flux with low-rank spatial distribution. The study verifies the capacity of the proposed method by theoretical analysis. Then, experiments were conducted on a carbon fiber composite panel with foreign inserts buried up to 5 mm deep. The sampled data are processed by the proposed method. The results are compared with existing methods such as phase-locked RPHF and EVBTF. The experimental results demonstrated that defects with normalized diameter-to-depth ratios as small as 0.9, barely detected with other available techniques, can reliably be detected by EVBTF-RPHF. The signal to noise ratio and the contrast are used as figure of merit to quantitatively compare the capacity of the proposed method with existing methods. However, the computation efficiency of the proposed algorithms needs further improvement.

Generalization ability is a critical property for practical motor fault diagnosis (FD). By converting time-series to images, several studies have made certain achievements. However, they still have following limitations. First, multisensor information fusion is rarely considered. Second, it is time consuming. To deal with the abovementioned problems, a multisensor-driven FD method based on visual features is proposed. Specifically, a color symmetrized dot pattern method is newly designed to infuse three multisensor signals to image. Next, a coarse and refined diagnosis framework is designed. In the coarse part, the color histogram features and a support vector machine (SVM) are utilized, and a threshold is selected to decide the coarse diagnostic samples. In the refined part, the gist (GIST) descriptor and another SVM are used to diagnose remaining samples. The results on induction motor and permanent magnet synchronous motor show that the proposed method achieved reliable diagnosis with relatively efficiency, and can generalize to different working conditions and noise.

To solve the problems of low efficiency and manual dependence of industrial motor winding testing, a joint scanning electromagnetic thermographic (JSET) method and a new quantitative evaluation algorithm are proposed to inspect defects automatically and assess detection capability. We establish a JSET-based defect inspection system including a joint scanning model and induction heating to simulate industrial assembly lines and acquire real-time thermograms. However, the acquired thermograms are misaligned in time and space, which cannot be used for dimension analysis. Therefore, a new 3-D data reconstruction algorithm is proposed to achieve accurate spatial-temporal alignment of the image sequence. Moreover, the parameters (scanning speed and excitation current) of the developed inspection system are optimized through obtaining the maximum inspection quantity. The new quantitative evaluation algorithm can measure the detection capability of different defects types, sizes, and positions by two features of significance and detected area. Experimental results show that the proposed methods can inspect multiple motor winding defects automatically and enhance the inspect efficiency.

In this article, defects in the production process of silicon photovoltaic (Si-PV) cells are urgently needed to be detected due to their serious impact on the normal generation of PV system. In view of the shortcomings, such as low-defect efficiency, few detection data, and high detection error rate in the existing industrial production line, the main research purpose of this article is to complete an intelligent classification method for efficient and innovative defect detection for Si-PV cells and modules. The purpose is to improve the detection efficiency of Si-PV cell, to ensure the safety and reliability of Si-PV cell production process, to achieve large number of Si-PV cell defects detection and classification. First, the eddy current thermography system of Si-PV cells is established. Second, principal component analysis, independent component analysis, and nonnegative matrix factorization algorithms are compared for thermography sequences processing. Third, LeNet-5, VGG-16, and GoogleNet models are compared for Si-PV cell defects classification. Finally, the results show that the proposed method have successful application in Si-PV cell defects detection and classification.

In the process of research, development, production, service, and maintenance of silicon photovoltaic (Si-PV) cells and the requirements for detection technology are becoming more and more important. This paper aims to investigate electromagnetic induction (EMI) and image fusion to improve the detection effect of electrothermography (ET) and electroluminescence (EL) of multidefects in Si-PV cells. First, the principles of ET, EL, and other physical processes including EMI, thermal radiation, and luminescence radiation are analyzed in this paper. ET and EL techniques after EMI improvement are used to detect different defects including scratch, broken gridline, surface impurity, hidden crack, and so on. The qualitative results show that EMI can greatly improve the defect detection ability of ET and EL. Then, an image-fusion rule based on L1 norm is proposed to fuse the sparse vector of the ET and EL images. The integration and complementarity of the two wavelength detection data are achieved. Finally, the image-fusion results of sparse representation (SR) algorithm is compared with discrete wavelet transform, curvelet transform, dual-tree complex wavelet transforms, and nonsubsampled contourlet transform. Five objective evaluation indexes including root mean square error, peak signal-to-noise ratio, correlation coefficient, mutual information, and structural similarity index are used to evaluate the fusion results. Overall evaluation results show that the SR algorithm is superior to the other algorithms.

Thermogram reconstruction methods based on one-dimensional models are widely used in data processing for thermography inspections. However, the surface temperature variances caused by thermal diffusion will be compatible with those caused by delamination whose normalized aspect (diameter-to-depth) ratio is close to 1 when the defect itself is very thin, about 0.15 mm in this research. This phenomenon makes the detection capacity of these methods reduced at such defects with small aspect ratios. The paper proposes a new reconstruction method, phase-locked restored pseudo heat flux (RPHF), for thermography inspection using square-wave optical stimulations. The theoretical analysis shows the independence of the method upon the effect of thermal diffusion blur at defect-free areas. Square-wave thermography tests are conducted on a carbon fiber composite panel with artificial delimitations buried up to 4 mm deep. The method is implemented on a private computer to deconvolute the RPHF kernel from the transformed thermogram data. The data are separated into two sets with a one-period phase shift to each other sequentially; a phase-locked substation is applied between the sets. The global signal-to-noise ratios obtained with the proposed method are compared to those obtained with Busse's lock-in phase images and those with thermographic signal reconstruction. The phase-locked RPHF gives the best global signal-to-noise ratios for normalized aspect ratio at 1.1 when sufficient heat is applied. It's concluded that the thermal diffusion effect at defect-free areas should be considered in thermography inspection for defects with a normalized aspect ratio at 1.1.

Impact damage, caused by low-energy impact, is inevitable during the whole life time of carbon fiber reinforced plastic (CFRP) material. However, the barely visible impact damage (BVID) is difficult to be detected by visual methods. Ultrasonic thermography (UT) is an emerging nondestructive testing technique that visualizes damage in thermal images captured by an infrared (IR) camera when the material is stimulated by ultrasound. However, noise and blurry edges around the high-temperature areas may cause confusion and lead to unreliable results in the thermal images of UT test. In this paper, an impact damage inspection method is proposed based on manifold learning for the CFRP material. Low-power ultrasonic excitation is used for this UT. The IR image sequences are processed as datasets in high-dimensional space. These datasets are reduced to lower dimensions by manifold learning to find the intrinsic structure in the two-dimensional manifold. Each dimension of the embedding manifold correlates highly with one degree of freedom underlying the original pixel: steady and random components. The steady component, which reflects the temperature rise caused by damage, is used for VID and BVID detection. The experimental system was set up, and CFRP plate specimens with different impact damage were tested. All the impact damage could be detected and shown in reconstructed static image with little noise. The proposed method using image sequences could provide a visualized, reliable, and effective impact damage inspection and localization means for CFRP material during manufacturing and in service.

Barely visible impact damage (BVID) is inevitable during either fabrication or lifetime of a carbon fiber reinforced plastic (CFRP) component. These flaws are usually difficult to be detected from the surface by visual inspection or machine vision based on a charge-coupled device or CMOS. In order to solve the problems of low efficiency, low sensitivity, and small detection area of the existing nondestructive testing (NDT) for BVID in CFRP, this paper proposes for the first time the integrated nonlinear ultrasound (NU) and vibrothermography (VT) NDT based on the shared excitation sources. The experimental system was built after introducing the principle of shared excitation based NU&VT NDT. The CFRP plates with 5, 15, and 25 J visible impact damage (VID) as well as 12 and 16 J BVID were tested using the integrated NU&VT. Experimental studies after signal processing have shown that all VID and BVID could be detected by the integrated NU&VT NDT, and the defection capability has a significant improvement after fast Fourier transform. The proposed method could provide a visualized and effective means for quality control and inspection of large-scaled and complex shape key components in manufacturing process and in service.

Defects can affect the generation efficiency and service life of photovoltaic (PV) cells and modules, and even can cause serious damage to grid connected PV power generation station. This paper proposes active electromagnetic induction infrared thermography defect detection methods for PV cells and modules, which have the advantages of noncontact, rapid, full-field, subtle, and quantitative detection. First, the mechanism of infrared radiation after electromagnetic induction of PV cells is described, and a digital EIIT system is established. Then, the thermal image sequences of PV cells and modules are obtained under pulse and lock-in modes of excitation. Fast Fourier transform, independent component analysis, and principal component analysis are used to deal with the thermal image sequences. Finally, the visual detection of defects, including scratches, hot spots, microcracks, surface impurities, and broken grids in PV cells and modules are realized. The experimental results have shown that the proposed method can distinguish the background noise and the defects very well, thus providing a reliable and rapid inspection means for the research, testing, manufacturing, service, and maintenance of PV cells and modules.

In order to solve the problems of low efficiency and small area of existing eddy current thermography industrial nondestructive testing, this work realizes in-field-of-view (FOV) dynamic scanning eddy current pulsed thermography (DSECPT) with the help of blind source separation (BSS) algorithms for continuous detection of large-scaled industrial components. The principle of FOV-DSECPT, including finite-length inductive heating of mobile coil, the reconstruction of transient temperature response, and feature extraction, is investigated. The original thermal images cannot be used for depth analysis; thus, new data reconstruction method with good adaptivity for manual movement was proposed. The emerging BSS algorithms, including independent component analysis and nonnegative matrix factorization, are employed to process the reconstructed data. Through experimental studies, images using various features from classical and BSS algorithms were compared. The proposed FOV-DSECPT could provide a visualized and effective means for quality control and inspection of large-scaled key components in both manufacturing and service processes.

Condition monitoring, nondestructive testing, and fault diagnosis are currently considered crucial processes for on-condition maintenance (OCM) to increase the reliability and availability of wind turbines and reduce the wind energy generation cost. Carbon fiber reinforced plastics (CFRPs) have been increasingly used to fabricate wind turbine blades. Delamination-type damage is inevitable during manufacture or in-service of a CFRP blade. This inner (subsurface) flaw, usually difficult to be detected by artificial visual inspection or machine vision based on CCD or CMOS, severely degrades the load-bearing capacity of a blade. Induction infrared thermography (IIT) is an emerging infrared machine vision inspection technology, which has the capability of insight to CFRP based on electromagnetic induction and heat conduction. This paper introduces photothermal thermal-wave radar (TWR) nondestructive imaging (NDI) to IIT, based on cross-correlation (CC) pulse compression and matched filtering and applies TWR principles to CFRP imaging inspection and diagnosis. The experimental studies carried out under the transmission mode have shown that TWR B-scan and phasegram can be used to inspect and diagnose subsurface delaminations in CFRP with improved signal-to-noise ratio (SNR) and shape identification. As a new machine vision inspection method, TWRI will play an important role in the OCM of the wind turbine blade.

This paper proposes an unsupervised method for diagnosing and monitoring defects in inductive thermography imaging system. The proposed method is fully automated and does not require manual selection from the user of the specific thermal frame images for defect diagnosis. The core of the method is a hybrid of physics-based inductive thermal mechanism with signal processing-based pattern extraction algorithm using sparse greedy-based principal component analysis (SGPCA). An internal functionality is built into the proposed algorithm to control the sparsity of SGPCA and to render better accuracy in sizing the defects. The proposed method is demonstrated on automatically diagnosing the defects on metals and the accuracy of sizing the defects. Experimental tests and comparisons with other methods have been conducted to verify the efficacy of the proposed method. Very promising results have been obtained where the performance of the proposed method is very near to human perception.

Imaging inspection is highly demanded in the optimization of industry processes. Optical imaging inspection is not applicable for inside defects, while infrared (IR) imaging inspection can provide information about internal structure of objects. Eddy current thermography is an emerging IR imaging inspection technique for conductive materials or objects. This paper presents eddy current volume heating thermography (ECVHT) and phase analysis for delamination inspection in carbon fiber reinforced plastics (CFRPs) based on the previously proposed eddy current pulsed phase thermography (ECPPT). The proposed method has been verified through experimental studies under both transmission and reflection modes. After discrete Fourier transform (DFT) of temperature responses, the phasegram and phase spectra can be used to image and characterize interface delamination in CFRP due to elimination of nonuniform heating effect and carbon fiber structures. With the whole temperature response processed by DFT, carbon fiber structures and delamination can be differentiated due to periodic oscillation of phase spectra. With temperature response in cooling phase processed by DFT, some characteristic features can be extracted to construct the new phase images according to the shape of phase spectra. In all, using ECVHT and phase analysis, imaging characterization for delamination can get much better performance than conventional visible optical inspection system and eddy current pulsed thermography.