Search Results (427)

In accordance with the PRISMA 2020 guidelines, this systematic review analyzed 73 publications (1997–2025) to summarize advancements in robotic aircraft skin inspection, focusing on the integrated pipeline from data acquisition to defect analysis. The review included studies on Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs) for external skin inspection, which present clear technical contributions, while excluding internal inspections and non-technical reports. Literature was retrieved from IEEE conferences, journals, and other academic databases, and key findings were summarized via the categorical analysis of motion planning, perception modules, and defect detection algorithms. Key limitations identified include the fragmentation of core technical modules, unresolved bottlenecks in dynamic environments, challenges in weak-texture and all-weather perception, and a lack of mature integrated systems with practical validation. The study concludes by advocating for future research in multi-robot heterogeneous collaborative systems, intelligent dynamic task scheduling, large model-based airworthiness assessment, and the expansion of inspection scenarios, all aimed at achieving fully autonomous and reliable operations. Full article

Three-dimensional ground-penetrating radar can quickly visualize the internal condition of the road; however, it faces challenges such as data splicing difficulties and image noise interference. Scanning antenna and lane size differences, as well as equipment and environmental interference, make the radar image difficult to interpret, which affects disease identification accuracy. For this reason, this paper focuses on road radar image splicing and noise reduction. The primary research includes the following: (1) We make use of backward projection imaging algorithms to visualize the internal information of the road, combined with a high-precision positioning system, splicing of multi-lane data, and the use of bilinear interpolation algorithms to make the three-dimensional radar data uniformly distributed. (2) Aiming at the defects of the low computational efficiency of the traditional adaptive median filter sliding window, a Deep Q-learning algorithm is introduced to construct a reward and punishment mechanism, and the feedback reward function quickly determines the filter window size. The results show that the method is outstanding in improving the peak signal-to-noise ratio, compared with the traditional algorithm, improving the denoising performance by 2–7 times. It effectively suppresses multiple noise types while precisely preserving fine details such as 0.1–0.5 mm microcrack edges, significantly enhancing image clarity. After processing, images were automatically recognized using YOLOv8x. The detection rate for transverse cracks in images improved significantly from being undetectable in mixed noise and original images to exceeding 90% in damage detection. This effectively validates the critical role of denoising in enhancing the automatic interpretation capability of internal road cracks. Full article

The ethanol oxidation reaction (EOR) is a key process for direct ethanol fuel cells (DEFCs), offering a high-energy-density and carbon-neutral pathway for sustainable energy conversion. However, the practical implementation of DEFCs is significantly hindered by the EOR due to its sluggish kinetics, complex multi-electron transfer pathways, and severe catalyst poisoning by carbonaceous intermediates. This review provides a comprehensive and mechanistically grounded overview of recent advances in EOR electrocatalysts, with a particular emphasis on the structure–activity relationships of noble metals (Pt, Pd, Rh, Au) and non-noble metals. The effects of catalyst composition, surface structure, and electronic configuration on C–C bond cleavage efficiency, product selectivity (C1 vs. C2), and CO tolerance are critically evaluated. Special attention is given to the mechanistic distinctions among different metal systems, highlighting how these factors influence reaction pathways and catalytic behavior. Key performance-enhancing strategies—including alloying, nanostructuring, surface defect engineering, and support interactions—are systematically discussed, with mechanistic insights supported by in situ characterization and theoretical modeling. Finally, this review identifies major challenges and emerging opportunities, outlining rational design principles for next-generation EOR catalysts that integrate high activity, durability, and scalability for real-world DEFC applications. Full article

Timely detection of road surface defects such as cracks and potholes is critical for ensuring traffic safety and reducing infrastructure maintenance costs. While recent advances in image-based deep learning techniques have shown promise for automated road defect detection, existing models remain limited to closed-set detection settings, making it difficult to recognize newly emerging or fine-grained defect types. To address this limitation, we propose an attribute-aware open-vocabulary crack detection (AOVCD) framework, which leverages the alignment capability of pretrained vision–language models to generalize beyond fixed class labels. In this framework, crack types are represented as combinations of visual attributes, enabling semantic grounding between image regions and natural language descriptions. To support this, we extend the existing PPDD dataset with attribute-level annotations and incorporate a multi-label attribute recognition task as an auxiliary objective. Experimental results demonstrate that the proposed AOVCD model outperforms existing baselines. In particular, compared to CLIP-based zero-shot inference, the proposed model achieves approximately a 10-fold improvement in average precision (AP) for novel crack categories. Attribute classification performance—covering geometric, spatial, and textural features—also increases by 40% in balanced accuracy (BACC) and 23% in AP. These results indicate that integrating structured attribute information enhances generalization to previously unseen defect types, especially those involving subtle visual cues. Our study suggests that incorporating attribute-level alignment within a vision–language framework can lead to more adaptive and semantically grounded defect recognition systems. Full article

Hyperbolic reflection features from ground-penetrating radar (GPR) data have been recognized as essential indicators for detecting hidden defects in the asphalt pavement. Computer vision and deep learning algorithms have been developed to detect and enhance the hyperbolic features of hidden defects. However, migrating existing hyperbolic feature detection methods using raw GPR data results in inaccurate predictions. Pre-processing raw GPR data using straightforward image processing methods could enhance the reflection features for fast and accurate hyperbolic detection during real-time GPR measurements. This study proposed accessible and straightforward image processing methods as GPR data preprocessing steps (such as the Sobel edge detector and histogram equalization) to assist existing computer vision algorithms for reflection feature enhancement during the GPR survey. Field tests were conducted, and several image processing methods with existing standard image processing libraries were applied. The proposed regions of the identified hyperbola signal-to-noise ratio (RIHSNR) were used to quantify the enhancement performance of hyperbolic feature detectability. Applying Sobel edge detection and Otsu’s thresholding to GPR data significantly improves detection accuracy and speed: mAP@0.5 rises from 0.65 to 0.85 for Faster R-CNN and from 0.72 to 0.88 for CBAM-YOLOv8 using the proposed computer vision methods as preprocessing steps. At the same time, inference time drops to 30 ms and 25 ms, respectively. Full article

Tunnel lining voids, a common latent defect induced by the coupling effects of complex geological, environmental, and load factors, pose severe threats to operational and personnel safety. Traditional detection methods relying on Ground-Penetrating Radar (GPR) combined with manual interpretation suffer from high subjectivity, low efficiency, frequent missed or false detections, and an inability to achieve real-time monitoring. Thus, this paper proposes an intelligent identification methodology for tunnel lining voids based on an improved version of YOLOv8. Key enhancements include integrating the RepVGGBlock module, dynamic upsampling, and a spatial context-aware module to address challenges from diverse void geometries—resulting from interactions between the environment, geology, and load—and complex GPR signals caused by heterogeneous underground media and the varying electromagnetic properties of materials, which obscure void–background boundaries, as well as interference signals from detection processes. Additionally, the C2f-Faster module reduces the computational complexity (GFLOPs), parameter count, and model size, facilitating edge deployment at detection sites to achieve real-time GPR signal interpretation for tunnel linings. Experimental results on a heavy-haul railway tunnel’s lining defect dataset show 11.57% lower GFLOPs, 14.55% fewer parameters, and 13.85% smaller weight files, with average accuracies of 94.1% and 94.4% in defect recognition and segmentation, respectively, meeting requirements for the real-time online detection of tunnel linings. Notably, the proposed model is specifically tailored for void identification and cannot handle other prevalent tunnel lining defects, which restricts its application in comprehensive tunnel health monitoring scenarios where multiple defects often coexist to threaten structural safety. Full article

The joint is the weak point of HV (high voltage) cable insulation systems; creep discharge between insulation layers of the cable joint, due to moisture intrusion, is one of the main defects leading to single-phase grounding. Carbonization on the insulation interface after creep discharge would lead to a short-circuit defect in the sheath loops and result in abnormal sheath current. In this study, a novel diagnostic criterion using the phasor difference of sheath currents at both ends of the same circuit is proposed. The coupling effect between the sheath and the conductor under defect conditions is considered, and the original lumped parameter model of the cable circuit is optimized. The cable parameters are further corrected using a genetic algorithm. The diagnostic criterion comprehensively accounts for the adverse effects of unequal cable segment lengths, load current fluctuations, grounding impedance, and phase voltage variations. When the phase angle fluctuation of the phasor difference is within 10° and the defect impedance is below 100 Ω, the defective joint can be accurately diagnosed by this method. The conclusion has been validated through PSCAD simulations, with a diagnostic accuracy above 97%. Even under 20 dB noise interference, the error increase remains within 2%. Full article

Ground-Penetrating Radar is a non-destructive tool for detecting subsurface structures. However, traditional image interpretation is often limited by medium complexity and noise. To improve detection efficiency and accuracy, this study combines deep learning techniques to develop an automatic embankment cavity identification system based on the YOLOv10 model. The research first constructs a training dataset containing GPR images of embankment cavities and expands the dataset through data augmentation strategies to enhance model adaptability. Subsequently, cross-validation is employed to fine-tune the hyperparameters of the YOLOv10 model, seeking optimal performance. The experimental results demonstrate that the YOLOv10 model successfully identifies cavities in radar images, achieving accuracy rates of nearly 90% and 97%. This study proves the potential of deep learning in GPR image analysis, effectively improving detection efficiency, accuracy, and automation levels, providing more reliable technical support for embankment safety inspection. Full article

Ground penetrating radar (GPR) is a non-destructive testing (NDT) method increasingly used for evaluating concrete structures by identifying internal flaws and embedded objects. This study presents a structured image-based methodology for interpreting GPR B-scan data using a practical flowchart designed to aid in distinguishing common subsurface anomalies. The methodology was validated through a laboratory experiment involving four concrete slabs embedded with simulated defects, including corroded rebar, hollow pipes, polystyrene sheets (to represent delamination), and hollow containers (to represent voids). Scans were performed using a commercially available device, and the resulting radargrams were analyzed based on signal reflection patterns. The proposed approach successfully identified rebar positions, spacing, and depths, as well as low-dielectric anomalies such as voids and polystyrene inclusions. Some limitations were noted in detecting non-metallic materials with weak dielectric contrast, such as hollow pipes. Overall, the findings demonstrate the reliability and adaptability of the proposed method in improving the interpretation of GPR data for structural diagnostics. The proposed methodology achieved a detection accuracy of approximately 90% across all embedded features, which demonstrates improved interpretability compared to traditional manual GPR assessments, typically ranging between 70 and 80% in similar laboratory conditions. Full article

Ensuring the structural integrity of wind turbines is crucial for the sustainability of wind energy. A significant challenge remains in transitioning from mere defect detection to objective, scalable criticality assessment for prioritizing maintenance. In this work, we propose a novel comprehensive framework that leverages multispectral unmanned aerial vehicle (UAV) imagery and a novel standards-aligned Fuzzy Inference System to automate this task. Our contribution is validated on two open research-oriented datasets representing small on- and offshore machines: the public AQUADA-GO and Thermal WTB Inspection datasets. An ensemble of YOLOv8n models trained on fused RGB-thermal data achieves a mean Average Precision (mAP@.5) of 92.8% for detecting cracks, erosion, and thermal anomalies. The core novelty, a 27-rule Fuzzy Inference System derived from the IEC 61400-5 standard, translates quantitative defect parameters into a five-level criticality score. The system’s output demonstrates exceptional fidelity to expert assessments, achieving a mean absolute error of 0.14 and a Pearson correlation of 0.97. This work provides a transparent, repeatable, and engineering-grounded proof of concept, demonstrating a promising pathway toward predictive, condition-based maintenance strategies and supporting the economic viability of wind energy. Full article

This study investigates the strain hardening and dislocation structure in the surface layers of C45 steel subjected to precision grinding at various depths. The aim was to assess how different grinding conditions influence the mechanical response and defect structure of ferrite. Nanoindentation was used to evaluate mechanical properties, while X-ray diffraction analysis provided data on the microstrain, crystallite size, and residual stress. The character and density of dislocations were further examined using modified Williamson–Hall and q-parameter analysis. The results revealed that the sample ground to a depth of 2 μm exhibited the highest density of statistically stored dislocations, as well as the lowest dislocation mobility. This condition also corresponded to the highest residual stresses and the greatest share of screw dislocations, indicating intense strain localization. In contrast, deeper grinding depths resulted in lower dislocation densities and reduced the strain energy. The observed trends highlight the formation of a dislocation-rich nanostructured layer in the shallowest ground region. These findings provide new insights into the mechanisms of surface hardening in ferritic steels and demonstrate how the depth of material removal during grinding governs the subsurface microstructure and strengthening effects. Full article

This study proposes a dual-element wideband circularly polarized (CP) slot-integrated multiple-input multiple-output (MIMO) antenna with an X-notch square-shaped artificial magnetic conductor (AMC) for dedicated short-range communications (DSRC) applications. The proposed antenna design consists of two substrate layers separated by an air gap. The upper layer features a dual-element coplanar waveguide-fed slot antenna and a defected ground structure decoupling isolator, while the lower layer comprises an 8 × 8 array of X-notch square-shaped elemental units, functioning as an AMC reflector. Characteristic mode analysis shows that circular polarization is produced by the dominant orthogonal mode pair (modes J₅ and J₆), whose modal significance exceeds 0.92 and whose characteristic angle separation is 82° around the 5.9 GHz DSRC band. An I-shaped slot embedded in the ground plane of the upper layer serves as a defected ground structure isolator to suppress mutual coupling between antenna elements. Meanwhile, the X-notch square AMC reflector enhances radiation characteristics and antenna gain. The measured return loss bandwidth and axial ratio bandwidth are 32% (4.72–6.61 GHz) and 21.18% (5.2–6.45 GHz), respectively. The dual-element antenna scheme achieves high isolation exceeding 19 dB, with a maximum gain of 8.6 dBic at 5.9 GHz. The envelop correlation coefficient remains below 0.003, while the diversity gain exceeds 9.98 dB. Full article

Internal conductive defects in composite insulators severely degrade their insulation performance and are considered concealed defects, posing a significant threat to the safe and stable operation of the power grid. Focusing on this issue, this study develops an electro-thermal multi-physical field simulation model and uses finite element analysis to investigate the electric field distribution and temperature rise characteristics. Composite insulator specimens with varying defect lengths were fabricated using the electrical erosion test. Charged tests were then conducted on these defective specimens, as well as on field-decommissioned specimens. The impact of internal conductive defects on the infrared, ultraviolet, and electric field distribution characteristics of composite insulators during operation was analyzed. The results indicate that the surface electric field of composite insulators with internal conductive defects becomes highly concentrated along the defect path, with a significant increase in electric field strength at the defect’s end. The maximum field strength migrates toward the grounded end as the defect length increases. Conductive defects lead to partial discharge and abnormal temperature rise at the defect’s end and the bending points of the composite insulator. The temperature rise predominantly manifests as “bar-form temperature rise,” with temperature rise regions correlating well with discharge areas. Conductive defects accelerate the decay-like degradation process of composite insulators through a positive feedback loop formed by the coupling of electric field distortion, Joule heating, material degradation, and discharge activity. This study identifies the key characteristics of electrical and temperature rise changes in insulators with conductive defects, reveals the deterioration evolution process and degradation mechanisms of insulators, and provides effective criteria for on-site diagnosis of conductive defects. Full article

Automated surface defect detection in steel manufacturing is pivotal for ensuring product quality, yet it remains an open challenge owing to the extreme heterogeneity of defect morphologies—ranging from hairline cracks and microscopic pores to elongated scratches and shallow dents. Existing approaches, whether classical vision pipelines or recent deep-learning paradigms, struggle to simultaneously satisfy the stringent demands of industrial scenarios: high accuracy on sub-millimeter flaws, insensitivity to texture-rich backgrounds, and real-time throughput on resource-constrained hardware. Although contemporary detectors have narrowed the gap, they still exhibit pronounced sensitivity–robustness trade-offs, particularly in the presence of scale-varying defects and cluttered surfaces. To address these limitations, we introduce MBY (MBDNet-Attention-YOLO), a lightweight yet powerful framework that synergistically couples the MBDNet backbone with the YOLO detection head. Specifically, the backbone embeds three novel components: (1) HGStem, a hierarchical stem block that enriches low-level representations while suppressing redundant activations; (2) Dynamic Align Fusion (DAF), an adaptive cross-scale fusion mechanism that dynamically re-weights feature contributions according to defect saliency; and (3) C2f-DWR, a depth-wise residual variant that progressively expands receptive fields without incurring prohibitive computational costs. Building upon this enriched feature hierarchy, the neck employs our proposed MultiSEAM module—a cascaded squeeze-and-excitation attention mechanism operating at multiple granularities—to harmonize fine-grained and semantic cues, thereby amplifying weak defect signals against complex textures. Finally, we integrate the Inner-SIoU loss, which refines the geometric alignment between predicted and ground-truth boxes by jointly optimizing center distance, aspect ratio consistency, and IoU overlap, leading to faster convergence and tighter localization. Extensive experiments on two publicly available steel-defect benchmarks—NEU-DET and PVEL-AD—demonstrate the superiority of MBY. Without bells and whistles, our model achieves 85.8% mAP@0.5 on NEU-DET and 75.9% mAP@0.5 on PVEL-AD, surpassing the best-reported results by significant margins while maintaining real-time inference on an NVIDIA Jetson Xavier. Ablation studies corroborate the complementary roles of each component, underscoring MBY’s robustness across defect scales and surface conditions. These results suggest that MBY strikes an appealing balance between accuracy, efficiency, and deployability, offering a pragmatic solution for next-generation industrial quality-control systems. Full article

Industrial Vision Inspection Systems (VISs) often struggle to adapt to increasing variability of modern manufacturing due to the inherent rigidity of their hardware architectures. Although the Reconfigurable Manufacturing System (RMS) paradigm was introduced in the early 2000s to overcome these limitations, designing such reconfigurable machines remains a complex, expert-dependent, and time-consuming task. This is primarily due to the lack of structured methodologies and the reliance on trial-and-error processes. In this context, this study proposes a novel theoretical framework to facilitate the design of fully reconfigurable handling systems for VISs, with a particular focus on fixture design. The framework is grounded in Model-Based Definition (MBD), embedding semantic information directly into the 3D CAD models of the inspected product. As an additional contribution, a general hardware architecture for the inspection of axisymmetric components is presented. This architecture integrates an anthropomorphic robotic arm, Numerically Controlled (NC) modules, and adaptable software and hardware components to enable automated, software-driven reconfiguration. The proposed framework and architecture were applied in an industrial case study conducted in collaboration with a leading automotive half-shaft manufacturer. The resulting system, implemented across seven automated cells, successfully inspected over 200 part types from 12 part families and detected more than 60 defect types, with a cycle below 30 s per part. Full article