Search Results (4,213)

Surface defect detection serves as one of the crucial auxiliary components in the quality control of varistors, and it faces real challenges such as the scarcity of defect samples, high labelling cost, and insufficient a priori knowledge, which makes unsupervised deep learning-based detection methods attract attention. However, existing unsupervised models have problems such as inaccurate defect localisation and a low recognition rate of subtle defects in the detection results. To solve the above problems, an unsupervised detection method (Var-MNDR) is proposed to reconstruct the normal distribution of surface defects of varistors under mask constraints. Firstly, on the basis of colour space as well as morphology, an image preprocessing method is proposed to extract the main body image of the varistor, and a mask-constrained main body pseudo-anomaly generation strategy is adopted so that the model focuses on the texture distribution of the main body region of the image, reduces the model’s focus on the background region, and improves the defect localisation capability of the model. Secondly, Kolmogorov–Arnold Networks (KANs) are combined with the U-Network (U-Net) to construct a segmentation sub-network, and the Gaussian radial basis function is introduced as the learnable activation function of the KAN to improve the model’s ability to express the image features, so as to realise more accurate defect detection. Finally, by comparing the four unsupervised defect detection methods, the experimental results prove the superiority and generalisation of the proposed method. Full article

This study introduces a non-destructive computer vision method for estimating postharvest quality parameters of oranges, including maturity index, soluble solid content (expressed in degrees Brix), and firmness. A novel image-based descriptor, termed Citrus Color Index—Gray Level Co-occurrence Matrix Texture Features (CCI–GLCM-TF), was developed by integrating the Citrus Color Index (CCI) with texture features derived from the Gray Level Co-occurrence Matrix (GLCM). By combining contrast, correlation, energy, and homogeneity across multiscale regions of interest and applying geometric calibration to correct image acquisition distortions, the descriptor effectively captures both chromatic and structural information from RGB images. These features served as input to an Adaptive Neuro-Fuzzy Inference System (ANFIS), selected for its ability to model nonlinear relationships and gradual transitions in citrus ripening. The proposed ANFIS models achieved R-squared values greater than or equal to 0.81 and root mean square error values less than or equal to 1.1 across all quality parameters, confirming their predictive robustness. Notably, representative models (ANFIS 2, 4, 6, and 8) demonstrated superior performance, supporting the extension of this approach to full-surface exploration of citrus fruits. The results outperform methods relying solely on color features, underscoring the importance of combining spectral and textural descriptors. This work highlights the potential of the CCI–GLCM-TF descriptor, in conjunction with ANFIS, for accurate, real-time, and non-invasive assessment of citrus quality, with practical implications for automated classification, postharvest process optimization, and cost reduction in the citrus industry. Full article

The increasing utilization of single-use plastics in the food sector poses serious environmental challenges. A circular economy approach, i.e., reusing packaging before recycling, offers a promising solution but raises concerns about cross-contamination between food products. This study investigates how repeated use and cleaning affect the surface topography of plastic food packaging and, in turn, how these changes influence cleaning efficiency and assessment. Recycled polyethylene terephthalate (rPET) trays were subjected to 20 industrial wash cycles with and without detergent concentration of 0.3% v/v at the following temperatures: 55 °C wash, 70 °C rinse. Surface roughness was measured using mechanical and optical techniques. Additionally, trays were roughened with sandpaper of varying grit sizes to simulate mechanical wear during consumer use. Cleanability was assessed using UV fluorescence imaging and adenosine triphosphate (ATP) assays. Results showed no significant increase in surface roughness after 20 wash cycles. However, artificially roughened surfaces retained more food residue, complicating cleaning. The application of UV fluorescence imaging proved more effective than ATP assays in detecting food residues on textured surfaces. These findings support the use of advanced imaging for evaluating the hygiene of reusable packaging and highlight key considerations for implementing circular reuse systems in food packaging. Full article

As a vital carrier of China’s intangible cultural heritage, restoring damaged embroidery fabrics is essential for the sustainable preservation of cultural relics. However, existing methods face persistent challenges, such as mask pattern mismatches and restoration size constraints. To address these gaps, this study proposes an embroidery image restoration framework based on enhanced generative adversarial networks (GANs). Specifically, the framework integrates a U-Net generator with a multi-scale discriminator augmented by an attention mechanism and dual-path residual blocks to significantly enhance texture generation. Furthermore, fabric damage was classified into three categories (hole-shaped, crease-shaped, and block-shaped), with complex patterns simulated through dynamic randomization. Grid-based overlapping segmentation and pixel fusion techniques enable arbitrary-dimensional restoration. Quantitative evaluations demonstrated exceptional performance in complex texture restoration, achieving a structural similarity index (SSIM) of 0.969 and a peak signal-to-noise ratio (PSNR) of 32.182 dB. Complementarily, eye-tracking experiments revealed no persistent visual fixation clusters in the restored regions, confirming perceptual reliability. This approach establishes an efficient digital conservation pathway that promotes resource-efficient and sustainable heritage conservation. Full article

In recent years, digital technologies have become indispensable tools for the preservation and documentation of architectural and cultural heritage. Traditional 3D modeling methods, such as photogrammetry and laser scanning, require specialized equipment and extensive manual processing. Neural Radiance Field, an AI-based technique, enables photorealistic 3D reconstructions from a limited set of 2D images. NeRF excels in cultural heritage documentation by effectively rendering reflective and translucent surfaces, which often pose challenges to conventional methods. These approaches significantly accelerate workflows, reduce costs, and minimize manual intervention, making them ideal for inaccessible or fragile sites. The application of NeRF combined with drone-acquired high-resolution images, as demonstrated in the Katolička Porta project in Novi Sad, produces highly detailed and accurate digital replicas. This integration also supports virtual restoration and texture enhancement, enabling non-invasive exploration of conservation scenarios. Katolička Porta, a historically significant site that has evolved over centuries, benefits from these advanced digital preservation techniques, which help maintain its unique architectural and cultural identity. This integration of technologies represents the future of cultural heritage conservation, offering innovative possibilities for visualization, research, and protection. Full article

The crystal structure, texture, martensitic transformation, and magnetic properties of magnetic shape-memory Heusler alloys of Ni_51−xMn_33.4In_15.6V_x (x = 0; 0.1; 0.3; 0.5; 1) were investigated. Experimental studies of the magnetic properties and meta-magnetostructural transition (martensitic transition—MT) confirm the main sensitivity of the martensitic transition temperature to vanadium doping and to an applied magnetic field. This makes this family of shape-memory alloys promising for use in numerous applications, such as magnetocaloric cooling and MEMS technology. Diffuse electron scattering was analyzed, and the structures of the austenite and martensite were determined, including the use of TEM in situ experiments during heating and cooling for an alloy with a 0.3 at.% concentration of V. In the austenitic state, the alloys are characterized by a high-temperature-ordered phase of the L2₁ type. The images show nanodomain structures in the form of tweed contrast and contrast from antiphase domains and antiphase boundaries. The alloy microstructure in the temperature range from the martensitic finish to 113 K consists of a six-layer modulated martensite, with 10 M and 14 M modulation observed in local zones. The morphology of the double structure of the modulated martensite structure inherits the morphology of the nanodomain structure in the parent phase. This suggests that it is possible to control the structure of the high-temperature austenite phase and the temperature of the martensitic transition by alloying and/or rapidly quenching from the high-temperature phase. In addition, attention is paid to maintaining fine interface structures. High-resolution transmission electron microscopy showed good coherence along the austenite–martensite boundary. 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

Nitrogen fertilization is decisive for maize productivity, fertilizer use efficiency, and sustainability, which calls for fast and nondestructive nutritional diagnosis. This study evaluated the classification of maize plant nutritional status from red, green, and blue (RGB) leaf images using texture attributes. A greenhouse experiment was conducted under a completely randomized factorial design with four nitrogen doses, one maize hybrid Pioneer 30F35, and four replicates, at two sampling times corresponding to distinct phenological stages, totaling thirty-two experimental units. Images were processed with the gray-level cooccurrence matrix computed at three distances 1, 3, and 5 pixels and four orientations 0°, 45°, 90°, and 135°, yielding eight texture descriptors that served as inputs to five supervised classifiers: an artificial neural network, a support vector machine, k nearest neighbors, a decision tree, and Naive Bayes. The results indicated that texture descriptors discriminated nitrogen doses with good performance and moderate computational cost, and that homogeneity, dissimilarity, and contrast were the most informative attributes. The artificial neural network showed the most stable performance at both stages, followed by the support vector machine and k nearest neighbors, whereas the decision tree and Naive Bayes were less suitable. Confusion matrices and receiver operating characteristic curves indicated greater separability for omission and excess classes, with D1 standing out, and the patterns were consistent with the chemical analysis. Future work should include field validation, multiple seasons and genotypes, integration with spectral indices and multisensor data, application of model explainability techniques, and assessment of latency and scalability in operational scenarios. Full article

Pansharpening fuses multispectral (MS) and panchromatic (PAN) remote sensing images to generate outputs with high spatial resolution and spectral fidelity. Nevertheless, conventional methods relying primarily on convolutional neural networks or unimodal fusion strategies frequently fail to bridge the sensor modality gap between MS and PAN data. Consequently, spectral distortion and spatial degradation often occur, limiting high-precision downstream applications. To address these issues, this work proposes a Hybrid Spectral–Structural Transformer Network (HSSTN) that enhances multi-level collaboration through comprehensive modelling of spectral–structural feature complementarity. Specifically, the HSSTN implements a three-tier fusion framework. First, an asymmetric dual-stream feature extractor employs a residual block with channel attention (RBCA) in the MS branch to strengthen spectral representation, while a Transformer architecture in the PAN branch extracts high-frequency spatial details, thereby reducing modality discrepancy at the input stage. Subsequently, a target-driven hierarchical fusion network utilises progressive crossmodal attention across scales, ranging from local textures to multi-scale structures, to enable efficient spectral–structural aggregation. Finally, a novel collaborative optimisation loss function preserves spectral integrity while enhancing structural details. Comprehensive experiments conducted on QuickBird, GaoFen-2, and WorldView-3 datasets demonstrate that HSSTN outperforms existing methods in both quantitative metrics and visual quality. Consequently, the resulting images exhibit sharper details and fewer spectral artefacts, showcasing significant advantages in high-fidelity remote sensing image fusion. Full article

Underwater crack detection in dam structures is of significant importance to ensure structural safety, assess operational conditions, and prevent potential disasters. Traditional crack detection methods face various limitations when applied to underwater environments, particularly in high dam underwater environments where image quality is influenced by factors such as water flow disturbances, light diffraction effects, and low contrast, making it difficult for conventional methods to accurately extract crack features. This study proposes a dual-stage underwater crack detection method based on Cycle-GAN and YOLOv11 called Edge-Enhanced Underwater CrackNet (E²UCN) to overcome the limitations of existing image enhancement methods in retaining crack details and improving detection accuracy. First, underwater concrete crack images were collected using an underwater remotely operated vehicle (ROV), and various complex underwater environments were simulated to construct a test dataset. Then, an improved Cycle-GAN image style transfer method was used to enhance the underwater images. Unlike conventional GAN-based underwater image enhancement methods that focus on global visual quality, our model specifically constrains edge preservation and high-frequency crack textures, providing a novel solution tailored for crack detection tasks. Subsequently, the YOLOv11 model was employed to perform object detection on the enhanced underwater crack images, effectively extracting crack features and achieving high-precision crack detection. The experimental results show that the proposed method significantly outperforms traditional methods in terms of crack detection accuracy, edge clarity, and adaptability to complex backgrounds, effectively improving underwater crack detection accuracy (precision = 0.995, F1 = 0.99762, mAP@0.5 = 0.995, and mAP@0.5:0.95 = 0.736) and providing a feasible technological solution for intelligent inspection of high dam underwater cracks. Full article

Against the backdrop of the continuous growth in global demand for mineral resources, efficient and accurate mineral exploration technologies are of paramount importance. Therefore, utilizing remote sensing technology, which features wide coverage, a non-contact nature, and multi-source data acquisition, is of great significance for conducting mineral resource exploration and prospecting research. This study focuses on the Tsagaankhairkhan copper–gold mining area in Mongolia and proposes a structural-alteration anomalies coupling mining prediction method based on the collaboration of multi-source remote sensing data. By comprehensively utilizing multi-source image data from Landsat-8, GF-2, and Sentinel-2, and employing methods such as principal component analysis (PCA), band ratio, and texture analysis, we effectively extracted structural information closely related to mineralization, as well as alteration anomaly information, including hydroxyl alteration anomalies and iron-staining alteration anomalies. Landsat-8 and Sentinel-2 data were employed to extract and mutually validate hydroxyl and iron-staining alteration anomaly information in the study area, thereby delineating alteration anomaly zones. By integrating the results of structural interpretation, the distribution of alteration anomaly information, and their spatial coupling characteristics, we explored the spatial coupling relationship between structural and alteration anomalies, analyzed their mineral control patterns, and identified 7 prospecting target areas. These target areas exhibit abundant mineral anomalies and favorable structural settings, indicating high metallogenic potential. The research findings provide crucial clues for the exploration of the Tsagaankhairkhan copper–gold mine in Mongolia and can guide future mineral exploration and development efforts. Full article

In human medicine, computer-aided diagnosis (CAD) is increasingly employed for screening, identifying, and monitoring early endoscopic signs of various diseases. However, its potential—despite proven benefits in human healthcare—remains largely underexplored in equine veterinary medicine. This study aimed to quantify endoscopic signs of pharyngeal lymphoid hyperplasia (PLH) as digital data and to assess their effectiveness in CAD of PLH in comparison and in combination with clinical data reflecting respiratory tract disease. Endoscopic images of the pharynx were collected from 70 horses clinically assessed as either healthy or affected by PLH. Digital data were extracted using an object detection-based processing technique and first-order statistics (FOS). The data were transformed using linear discriminant analysis (LDA) and classified with the random forest (RF) algorithm. Classification metrics were then calculated. When considering digital and clinical data, high classification performance was achieved (0.76 accuracy, 0.83 precision, 0.78 recall, and 0.76 F1 score), with the highest importance assigned to selected FOS features: Number of Objects and Neighbors, and Tracheal Auscultation. The proposed protocol of digitizing standard respiratory tract diagnostic methods provides effective discrimination of PLH grades, supporting the clinical value of CAD in veterinary medicine and paving the way for further research in digital medical diagnostics. Full article

In recent years, large multimodal models (LMMs), such as ChatGPT 4o and DeepSeek R1—artificial intelligence systems capable of multimodal (e.g., image and text) human–computer interaction—have gained traction in industrial and civil engineering applications. Concurrently, insufficient real-world drone-view data (specifically close-distance, high-resolution imagery) for civil engineering scenarios has heightened the importance of artificially generated content (AIGC) or synthetic data as supplementary inputs. AIGC is typically produced via text-to-image generative models (e.g., Stable Diffusion, DALL-E) guided by user-defined prompts. This study leverages LMMs to interpret key parameters for drone-based image generation (e.g., color, texture, scene composition, photographic style) and applies prompt engineering to systematize these parameters. The resulting LMM-generated prompts were used to synthesize training data for a You Only Look Once version 8 segmentation model (YOLOv8-seg). To address the need for detailed crack-distribution mapping in low-altitude drone-based monitoring, the trained YOLOv8-seg model was evaluated on close-distance crack benchmark datasets. The experimental results confirm that LMM-prompted AIGC is a viable supplement for low-altitude drone crack monitoring, achieving >80% classification accuracy (images with/without cracks) at a confidence threshold of 0.5. Full article

Defect detection in lithium-ion battery (LIB) welding presents unique challenges, including scale heterogeneity, subtle texture variations, and severe class imbalance. We propose a multi-scale convolutional framework that integrates EfficientNet-B0 for lightweight representation learning, PANet for cross-scale feature aggregation, and a YOLOv8 detection head augmented with multi-head attention. Parallel dilated convolutions are employed to approximate self-similar receptive fields, enabling simultaneous sensitivity to fine-grained microstructural anomalies and large-scale geometric irregularities. The approach is validated on three datasets including RIAWELC, GC10-DET, and an industrial LIB defects dataset, where it consistently outperforms competitive baselines, achieving 8–10% improvements in recall and F1-score while preserving real-time inference on GPU. Ablation experiments and statistical significance tests isolate the contributions of attention and multi-scale design, confirming their role in reducing false negatives. Attention-based visualizations further enhance interpretability by exposing spatial regions driving predictions. Limitations remain regarding fixed imaging conditions and partial reliance on synthetic augmentation, but the framework establishes a principled direction toward efficient, interpretable, and scalable defect inspection in industrial manufacturing. Full article

This paper proposes a novel perceptual image hashing scheme that robustly combines global structural features with local texture information for image authentication. The method starts with image normalization and Gaussian filtering to ensure scale invariance and suppress noise. A saliency map is then generated from a color vector angle matrix using a frequency-tuned model to identify perceptually significant regions. Local Binary Pattern (LBP) features are extracted from this map to represent fine-grained textures, while rotation-invariant Zernike moments are computed to capture global geometric structures. These local and global features are quantized and concatenated into a compact binary hash. Extensive experiments on standard databases show that the proposed method outperforms state-of-the-art algorithms in both robustness against content-preserving manipulations and discriminability across different images. Quantitative evaluations based on ROC curves and AUC values confirm its superior robustness–uniqueness trade-off, demonstrating the effectiveness of the saliency-guided fusion of Zernike moments and LBP for reliable image hashing. Full article