Search Results (816)

The humid coastal Lingnan region of South China, including the Chaoshan area of eastern Guangdong, is frequently exposed to disaster-prone weather conditions such as high humidity, typhoon-related winds, heavy rainfall, and salt-laden coastal air. These long-term environmental exposures may contribute to surface deterioration risks of architectural heritage. Located in Shantou, Yingchuan Shijia has shown five visible surface deterioration types—cracks, staining, saltpetering, plants, and spalling—under the combined influence of environmental exposure, material aging, previous disturbance, and insufficient maintenance. To address the limitations of manual inspection, this study explores a conservation-oriented intelligent workflow integrating YOLO-based detection, digital documentation, and screening-level conservation interpretation. Digital documentation used UAV imagery, mobile LiDAR scanning, measured drawings, and SketchUp-based three-dimensional modeling. The dataset was built in three stages: a 99-image preliminary dataset, where YOLOv8 showed only basic learning capability with low performance metrics, including Precision of 33.0 ± 3.0%, Recall of 28.0 ± 1.0%, mAP50 of 25.0 ± 1.0%, and mAP50-95 of 11.0 ± 1.0%; a 362-image non-augmented case-study dataset, where YOLOv8 still showed limited performance, with mAP50 of 20.0 ± 1.0% and mAP50-95 of 8.0 ± 1.0%; and a final YOLO-format case-study dataset of 2000 images after training-set-only augmentation using 11 geometric and photometric transformation methods. After augmentation, YOLOv8 mAP50 increased to 62.0 ± 2.0%. Under the same augmented-data condition, YOLOv13 showed Precision of 89.0 ± 1.0%, Recall of 77.0 ± 1.0%, mAP50 of 84.0 ± 1.0%, and mAP50-95 of 65.0 ± 1.0%, indicating relatively higher validation performance than YOLOv8. In the normalized confusion matrix, the background missed-detection values for cracks and saltpetering were 0.29 and 0.22, respectively, indicating that weak-feature and low-contrast deterioration types remained challenging. Based on YOLOv13, a mini program was developed to organize detection outputs and provide field-oriented preliminary conservation hints. Overall, this study provides a preliminary workflow linking digital collection, image-based deterioration detection, Grad-CAM visualization, and assisted field recording for the preventive conservation of Chaoshan overseas-Chinese residences in humid coastal heritage environments. Full article

Mining-subsidence-induced surface cracks pose substantial risks to ecological systems, infrastructure stability, and mining safety. Their thin, elongated, discontinuous, and low-contrast characteristics make accurate detection from unmanned aerial vehicle (UAV) imagery challenging, particularly under complex environmental conditions. This study proposes an enhanced YOLO11n framework for detecting surface cracks in mining subsidence areas. Switchable Atrous Convolution (SAConv) was incorporated to strengthen multi-scale feature extraction, while Cascaded Group Attention (CGA) was introduced to suppress background interference and improve feature discrimination, and Shape-IoU loss was adopted to enhance the localization of slender crack targets. The model was evaluated using 5000 annotated UAV images collected in the Zhungeer mining area. It achieved a precision of 85.6%, a recall of 77.9%, an mAP@0.5 of 84.3%, and an F1-score of 81.6%. Compared with the baseline YOLO11n, precision, recall, and mAP@0.5 increased by 1.4, 4.6, and 3.2 percentage points, respectively. Cross-dataset evaluation on the public Crack500 dataset further demonstrated improved robustness under domain variation. These results indicate that the proposed framework improves the detection and localization of slender and discontinuous cracks in complex mining environments, supporting its application in UAV-based geological hazard monitoring. Full article

To address challenges such as complex background interference, limited long-range modeling capabilities of CNNs, and poor generalization in steel-concrete cross-material scenarios, this study proposes an enhanced detection framework. This framework integrates a TAPFormer with morphological reasoning rules. The method utilizes TAPFormer as the backbone network. It captures global topological features of cracks through a Task-Aware Query mechanism. This approach compensates for the deficiencies of traditional convolutional operators in modeling the continuity of thin and long cracks. Furthermore, a mask reasoning module based on geometric priors is developed to handle unstructured interferences, such as marker pen marks, welds, and concrete holes. This module defines logical criteria, including edge curvature consistency, axial aspect ratios, and endpoint extension directions. These criteria are used to perform topological repair and filter false positives in the initial segmentation masks. A hybrid dataset containing 4500 cross-material damage images was used for validation. The results show that the proposed method achieves a mean IoU of 86.72% and an F1-score of 90.36%. Notably, the method filters over 91.0% of false positives caused by manual marker pen marks in interference-rich scenarios. Compared to mainstream state-of-the-art models, the IoU improves by at least 5.48%. The results show that the proposed framework improves the robustness and logical self-consistency of crack identification in complex engineering environments. Full article

Early identification of thermal and electrical anomalies in grid-connected photovoltaic (PV) systems is becoming increasingly important to reduce energy losses, limit power quality (PQ) degradation, and avoid excessive operating stress on power electronic converters. Conventional electrical monitoring methods can provide overall performance information, but they are generally unable to detect and localize early-stage defects occurring at module or cell level. In this context, the present study proposes an integrated diagnostic framework that combines non-destructive infrared thermography (IRT) with advanced electrical signal processing techniques for PV condition monitoring. The proposed approach correlates thermographic information, capable of revealing defects such as hotspots, cell cracks, and bypass diode failures, with high-frequency electrical signal analysis based on frequency-domain and time–frequency methods, together with deep learning-driven thermographic segmentation. By associating thermal acquisitions with electrical PQ indicators, the framework enables the early detection of physical defects linked to inefficient Maximum Power Point Tracking (MPPT) operation and progressive degradation of PV system performance. The methodology was experimentally validated on a grid-connected photovoltaic installation under different fault conditions, including hotspots, bypass diode anomalies, and localized overheating effects, demonstrating the potential of the proposed approach for predictive maintenance and intelligent PV monitoring applications. The obtained results indicate that the proposed framework improves the reliability of photovoltaic fault detection by combining thermographic inspection with advanced electrical signal analysis and AI-based defect interpretation, thus supporting predictive maintenance strategies in smart PV infrastructures. The proposed approach demonstrates image segmentation capabilities, as evidenced by a precision (PA) of 96.88%, a mean IoU (mIoU) of 77.83% and a macro F1-score of 87.47%. The proposed framework maintained reduced computational requirements compatible with real-time monitoring applications. Full article

Surface cracks on mandrel bars affect product quality and production stability in seamless steel pipe manufacturing. Existing vision-based methods mainly rely on bounding-box detection, which is insufficient for precise crack delineation and geometric characterization. This study proposes a lightweight segmentation framework for online mandrel bar crack inspection using grayscale industrial images. Based on YOLO11n-seg, the framework incorporates single-channel input adaptation, lightweight network reconfiguration, and crack-oriented feature enhancement to improve the extraction of weak, thin, and irregular cracks while reducing computational cost. A dedicated industrial dataset and a sample-balancing strategy are introduced to alleviate severe crack–background imbalance. Based on the predicted pixel-level masks, crack area, projected length, maximum width, and average width are calculated for online evaluation. Experimental results show that the proposed method achieves a mask mAP@0.5 of 88.5%, a false negative rate of 1.72%, and real-time inference at 204 FPS with 3.01 GFLOPs. Field deployment further demonstrates the effectiveness of the proposed framework for online crack inspection and geometric parameter calculation of mandrel bars. Full article

Concrete cracks are ubiquitous in practical engineering structures and continuously affect structural safety and durability. Crack images provide important visual evidence of damage evolution; however, crack images alone are insufficient to determine the residual load-bearing capacity of concrete members. Although the development of deep learning algorithms has significantly improved the automatic detection of concrete surface cracks, most existing methods remain limited to the extraction of crack geometric features and lack a direct connection with mechanical performance. To explore the relationship between image-based crack geometry and mechanical response, this study combines U-Net-based crack segmentation, OpenCV-based crack geometry extraction, and phase-field fracture simulation to establish a preliminary visual–mechanical framework for plain concrete beams. In this framework, surface crack images are first segmented using a U-Net model, and crack length, average width, and propagation path are extracted from the predicted binary masks. The extracted crack length is then used as the primary variable to match the observed crack state with the phase-field crack evolution sequence. Once the corresponding simulation stage is identified, the associated load level and residual load-bearing capacity can be obtained from the simulated load–crack mouth opening displacement (Load–CMOD) response. Through a mixed-mode I–II fracture test, the crack geometric features extracted by deep learning are compared with the phase-field simulation results. The results show that the error in crack length is within 2.5%. Meanwhile, the relative error between the simulated peak load and the experimental value was 1.57%, which preliminarily verified the correlation between image-based crack information and the load-bearing capacity of plain concrete beams. The method is further applied to a Mode I fracture test without recorded load-bearing capacity data. By mapping the crack length identified from the image, namely 36.89 mm, to the phase-field evolution sequence, the load-bearing capacity of the member at this stage is estimated to be 74.4% of the peak load. The results indicate that the crack geometry extracted from images can be correlated with phase-field crack evolution, thereby supporting preliminary residual load-bearing capacity assessment of plain concrete beams. However, the proposed framework should be regarded as a case-level feasibility study rather than a general structural assessment method. Before broader engineering application, further validation using synchronized crack image sequences, crack mouth opening displacement (CMOD) measurements, and load records is required. Full article

Image enhancement is of pivotal importance in the detection of defects in wood veneers. However, acquired images frequently exhibit signs of blurring, uneven illumination, and insufficient contrast, which can lead to a reduction in the accuracy of defect recognition. In this study, an algorithm based on Grünwald–Letnikov (G–L) fractional-order differentiation is proposed for the enhancement of wood veneer defect images. Initially, the gain characteristics of differential amplitude-frequency responses on high- and low-frequency image components are analyzed, and the feasibility of the method is demonstrated by linking these characteristics with the frequency-domain distributions of live knot, dead knot, and crack defects. Secondly, an eight-direction mask operator is constructed based on the G–L definition, and a DC component preservation factor is introduced to eliminate the luminance drift caused by mask truncation. The application of the mask is performed independently on the R, G, and B channels, and a dynamic blending mechanism is designed to achieve a balance between texture enhancement and structural fidelity. Finally, a set of six evaluation metrics (AG, E, PSNR, RMSE, SSIM, and VIF) is employed to assess the quality of enhanced images. The proposed algorithm is then compared with five existing algorithms (SSR, MSR, MSRCR, CLAHE, and AGC) under both noise-free and additive white Gaussian noise conditions. The findings indicate that the G–L fractional-order differentiation algorithm facilitates a more balanced representation of image features, thereby enhancing contrast, brightness, and textural contours. This approach results in more authentic color reproduction and superior visual quality. Full article

Civil infrastructure plays a critical role in ensuring societal safety and economic development. However, structural damages such as cracks inevitably occur during long-term service. Traditional manual inspection methods are insufficient to meet the demands of large-scale and routine monitoring. Unmanned Aerial Vehicles (UAV) remote sensing has become an important approach for Structural Health Monitoring (SHM), owing to its high spatial resolution imaging capability and superior operational flexibility. Nevertheless, existing studies focus on optimizing individual algorithms, lacking a systematic analysis oriented toward multi-scenario engineering applications. Therefore, we present a comprehensive review of UAV-based crack detection techniques for infrastructure using remote sensing imagery. First, publicly available datasets, UAV platforms, and evaluation metrics are systematically summarized. Then a multi-level visual analysis framework for UAV inspection is established. The framework categorizes existing methodologies into five levels: image-level classification, object-level detection, pixel-level segmentation, geometric quantification, and three-dimensional (3D) reconstruction, followed by a systematic evaluation of representative methods. Furthermore, the applicability of different methods across diverse scenarios, including bridges, pavements, dams, building facades and wind turbine blades, is systematically explored. Finally, the key challenges and future research directions are discussed. This review aims to provide a systematic theoretical foundation and methodological reference for advancing UAV-based infrastructure crack inspection from algorithm development toward practical multi-scenario engineering applications. Full article

Continuous manufacturing has emerged as the prevailing paradigm in the modern pharmaceutical industry, imposing stringent demands for efficient, real-time inspection methods. Furthermore, deploying high-performance deep learning models on industrial edge devices remains challenging due to computational constraints and the difficulty of detecting micro-defects (e.g., micro-cracks and spots). This paper proposes EMN-net, a lightweight defect detection model built upon the YOLOv8n architecture. The proposed algorithm integrates a MobileNetV3 backbone, the Efficient Local Attention (ELA) mechanism and the Normalized Wasserstein Distance (NWD) loss function to balance computational efficiency with sensitivity toward micro-defects. Evaluated on a self-built industrial tablet dataset expanded to 3086 images, EMN-net achieves an mAP50 of 97.8%, representing a 2.5% improvement over the baseline YOLOv8n. the computational complexity is reduced to 4.4 GFLOPs, while the inference throughput reaches 118 FPS, satisfying the real-time requirements of high-speed production lines. Additionally, the model exhibits improved robustness under simulated motion blur and sensor noise. EMN-net presents a balanced automated visual inspection solution for edge devices in continuous pharmaceutical manufacturing. Full article

With the rapid development of unmanned aerial vehicle (UAV) remote sensing and deep learning, road crack detection has become an important component of road condition assessment and intelligent road maintenance. However, accurately detecting cracks from UAV images remains challenging due to complex background environments, slender crack structures, blurred boundaries, and irregular crack shapes and orientations. Traditional methods that rely solely on visual information often struggle to achieve stable and accurate detection performance under these conditions. To address these challenges, this paper proposes a Multimodal Crack Geometry-Consistent Network (MCGC-Net) for high-precision road crack detection in complex road scenes. First, a UAV-based multimodal road crack dataset with image-text annotations is constructed. Specifically, crack-related textual descriptions are automatically generated from crack annotations using predefined semantic templates, which summarize crack morphology, spatial distribution characteristics, and structural properties. These semantic descriptions provide high-level semantic prior information for crack representation learning. Second, a Multimodal Contrastive Semantic Gating module (MCSG) is introduced to leverage automatically generated crack semantic descriptions and in-batch image-text semantic differences to guide visual feature learning, thereby improving the discrimination between crack and non-crack regions under complex background conditions. Furthermore, a Crack-Aware Slenderness Loss (CASL) is proposed to explicitly constrain slenderness consistency between predicted boxes and ground-truth boxes, improving localization stability for slender crack targets. In addition, a KAN-based Nonlinear Channel Attention mechanism (KAN-CA) is introduced to enhance feature representation capability for complex crack structures. Experimental results demonstrate that the proposed MCGC-Net effectively improves crack detection accuracy and structural representation capability under complex road environments. The proposed method provides a practical and reliable solution for UAV-based intelligent road crack detection. Full article

Dynamic three-dimensional (3-D) reconstruction of small objects moving at high speed is fundamentally limited by the number of viewpoints that a fixed camera array can provide at any single time instant. When the camera count is insufficient, single-frame multi-view stereo produces incomplete or inaccurate geometry. This paper proposes a multi-frame temporal integration approach that overcomes this limitation by exploiting the rigid-body assumption: because a falling object maintains its shape across consecutive frames, images captured at different time instants can be combined into a single, viewpoint-enriched reconstruction. A three-layer circular array of 32 synchronized RGB cameras captures 1440 × 1080 images at 160 fps, and a free-fall-oriented algorithm automatically detects active frames, selects informative temporal windows, and feeds the accumulated multi-frame images into a structure-from-motion and multi-view stereo (SfM-MVS) pipeline, effectively multiplying the number of viewpoints without additional hardware. The algorithm simultaneously recovers the 6-DOF pose trajectory of each object from the SfM-estimated camera parameters. Progressive accumulation experiments on freely falling soybeans (approximately 9–10 mm diameter) show that a single 32-camera frame already achieves an F-score exceeding 0.97 at a 0.5 mm threshold against an industrial structured-light scanner reference, and that accumulating additional temporal frames reaches a stable convergence plateau with both objects reaching a plateau F-score of 0.984. Beyond approximately one to two accumulated frames, additional frames yield diminishing returns, confirming that a small number of temporal frames is sufficient for convergent sub-millimeter accuracy. Across 30 independent free-fall trials with three objects, the system achieves an overall mean error of $0.146\pm 0.033$ mm and an overall F-score of $0.980\pm 0.006$—a mean relative error of approximately 1.6% on 8–10 mm targets—and fine surface features such as structural cracks are resolved at a fidelity sufficient for visual defect identification. These results establish rigid-body multi-frame temporal integration as an effective strategy for high-throughput, non-contact 3-D inspection of small objects in motion. Full article

Unet, a deep learning architecture, has become one of the most widely used models for crack detection in the tunneling field. Although it performs well in overall crack image segmentation, it still has issues of limited feature expression capability and inaccurate segmentation. To address these problems, DTA-Unet was proposed based on dynamic convolution decomposition (DCD) and triple attention (TA). Firstly, the model used Unet as the baseline network and replaced traditional convolutions in the encoding-decoding process with DCD to enhance its feature extraction ability. Secondly, TA was combined with attention gate (AG) in the skip connections of the network, eliminating redundant information in spatial and channel dimensions to highlight the crack area. Finally, the proposed model was tested on crack datasets and compared with the conventional Unet model, image processing algorithms, and other deep neural network models in terms of detection performance on the datasets. The results show that it outperforms other advanced methods in crack detection performance. The proposed method is of significance to the maintenance of shield tunnel cracks. Full article

To address the problem of road crack detection in low-quality pavement images, existing semantic segmentation methods still have shortcomings such as missed crack detection and inaccurate localization due to weak crack boundaries, low contrast, and complex pavement texture. To address these limitations, this study proposes a crack segmentation model based on feature integration and a triple attention mechanism. The model uses DeepLabv3+ as the backbone network and introduces the proposed three-dimensional interactive attention module after feature extraction. The attention module enhances the extraction of key features related to the spatial location and morphological details of cracks, thereby improving the ability of crack location. A hierarchical feature integration branch is introduced in the cross-layer connection, and a dimension-aware selective fusion module is used to enhance the saliency of small cracks in complex backgrounds. In addition, the proposed multi-group dilation feature fusion module is introduced to improve the multi-scale modeling of small and slender cracks and reduce background interference. The experimental results on Crack500 and GAPS384 datasets show that the proposed model achieves better overall segmentation performance than the comparison model, especially in reducing the missed detection of weak, small, and discontinuous cracks in low-quality pavement images. Complexity analysis further shows that the proposed model maintains practical inference efficiency rather than relying on too large a model size. These results show that the proposed method provides an effective solution for low-quality road crack segmentation, but it still needs to be further verified in actual detection scenarios. Full article

Accurate underwater crack detection is essential for the condition monitoring of hydraulic structures. However, reliable detection in underwater inspection imagery remains challenging because of low visibility, complex backgrounds, large-scale variation, and irregular crack morphology. To improve detection under these conditions, we develop MA-YOLO, a YOLOv11-based detector that adapts feature representation to underwater crack morphology. The proposed method integrates a broader receptive field spatial pyramid pooling module to enhance multi-scale feature extraction, a morphological attention module to improve the representation of irregular crack patterns, and an extra-large detection head to better detect magnified cracks in close-range underwater images. Experiments on the underwater crack dataset (UCD) show that MA-YOLO outperforms both conventional detectors and recent underwater object-specific detectors. Relative to YOLOv11, MA-YOLO increases mAP@0.5 from 91.2% to 92.9% and mAP@0.5:0.95 from 60.0% to 63.0%, while maintaining a lightweight architecture and real-time inference capability. The results demonstrate the effectiveness of morphology-adaptive feature modeling for image-based underwater crack detection and its potential for practical monitoring of submerged hydraulic structures. Full article

Murals are not merely independent visual artworks. Rather, they are an integral part of architectural heritage, directly attached to buildings’ structural elements, such as brick walls and vaults. However, murals are susceptible to various building-related types of damage, including structural cracks and moisture-induced peeling, due to long-term exposure to environmental factors and geological changes. As the progressive deterioration of these murals hastens the loss of mural value, professional assessment and restoration are urgently required. To tackle the issues of low efficiency in traditional structural damage detection and the absence of predictable repair plans, this paper presents a semi-automatic building-mural protection solution that integrates morphological assessment of mural deterioration with computer vision technology. This study establishes an image prediction system that integrates intelligent damage identification with virtual restoration. First, employing the PaddleSeg deep learning framework and the DeepLabv3 semantic segmentation model, this study used existing mural damage datasets to build a recognition model. The model allows for intelligent identification and labeling of multiple damage types. Subsequently, relying on the ComfyUI platform, Stable Diffusion was used to construct a virtual restoration model. LoRA (low-rank adaptation) technology was introduced to fine-tune the model specifically for the mural style, thus enhancing the directivity and accuracy of virtual restoration. Finally, by applying the results of the recognition model to the virtual restoration model, this study built an integrated system for mural damage diagnosis and virtual restoration. The results show that the damage recognition model achieved a mean intersection over union (mIoU) of 47.8% and a pixel accuracy of 77.97% on the test set, validating the feasibility of using semantic segmentation for mural damage detection. This study presents an integrated workflow framework integrating automatic damage identification and intelligent repair. As an expert-assisted tool, this framework shows application potential for preliminary exploration of mural disease diagnosis and virtual restoration plans, providing technical references for the digital protection of cultural heritage. Full article