Search Results (136)

To address the challenges of manual inspection dependency, low efficiency, and high costs in evaluating the surface grinding quality of composite materials, this study investigated machine vision-based surface recognition algorithms. We proposed a multi-scale texture fusion analysis algorithm that innovatively integrated luminance analysis with multi-scale texture features through decision-level fusion. Specifically, a modified Rayleigh parameter was developed during luminance analysis to rapidly pre-segment unpolished areas by characterizing surface reflection properties. Furthermore, we enhanced the traditional Otsu algorithm by incorporating global grayscale mean (μ) and standard deviation (σ), overcoming its inherent limitations of exclusive reliance on grayscale histograms and lack of multimodal feature integration. This optimization enables simultaneous detection of specular reflection defects and texture uniformity variations. To improve detection window adaptability across heterogeneous surface regions, we designed a multi-scale texture analysis framework operating at multiple resolutions. Through decision-level fusion of luminance analysis and multi-scale texture evaluation, the proposed algorithm achieved 96% recognition accuracy with >95% reliability, demonstrating robust performance for automated surface grinding quality assessment of composite materials. Full article

Jute, a vital lignocellulosic fiber crop with substantial industrial and ecological relevance, continues to suffer considerable yield and quality degradation due to pervasive foliar pathologies. Traditional diagnostic modalities reliant on manual field inspections are inherently constrained by subjectivity, diagnostic latency, and inadequate scalability across geographically distributed agrarian systems. To transcend these limitations, we propose DERIENet, a robust and scalable classification approach within a deep ensemble learning framework. It is meticulously engineered by integrating three high-performing convolutional neural networks—ResNet50, InceptionV3, and EfficientNetB0—along with regularization, batch normalization, and dropout strategies, to accurately classify jute leaf diseases such as Cercospora Leaf Spot, Golden Mosaic Virus, and healthy leaves. A key methodological contribution is the design of a novel augmentation pipeline, termed Geometric Localized Occlusion and Adaptive Rescaling (GLOAR), which dynamically modulates photometric and geometric distortions based on image entropy and luminance to synthetically upscale a limited dataset (920 images) into a significantly enriched and diverse dataset of 7800 samples, thereby mitigating overfitting and enhancing domain generalizability. Empirical evaluation, utilizing a comprehensive set of performance metrics—accuracy, precision, recall, F1-score, confusion matrices, and ROC curves—demonstrates that DERIENet achieves a state-of-the-art classification accuracy of 99.89%, with macro-averaged and weighted average precision, recall, and F1-score uniformly at 99.89%, and an AUC of 1.0 across all disease categories. The reliability of the model is validated by the confusion matrix, which shows that 899 out of 900 test images were correctly identified and that there was only one misclassification. Comparative evaluations of the various ensemble baselines, such as DenseNet201, MobileNetV2, and VGG16, and individual base learners demonstrate that DERIENet performs noticeably superior to all baseline models. It provides a highly interpretable, deployment-ready, and computationally efficient architecture that is ideal for integrating into edge or mobile platforms to facilitate in situ, real-time disease diagnostics in precision agriculture. Full article

Underwater inspection images of hydraulic structures often suffer from haze, severe color distortion, low contrast, and blurred textures, impairing the accuracy of automated crack, spalling, and corrosion detection. However, many existing enhancement methods fail to preserve structural details and suppress noise in turbid environments. To address these limitations, we propose a compact image enhancement framework called Wavelet Fusion with Sobel-based Weighting (WWSF). This method first corrects global color and luminance distributions using multiscale Retinex and gamma mapping, followed by local contrast enhancement via CLAHE in the L channel of the CIELAB color space. Two preliminarily corrected images are decomposed using discrete wavelet transform (DWT); low-frequency bands are fused based on maximum energy, while high-frequency bands are adaptively weighted by Sobel edge energy to highlight structural features and suppress background noise. The enhanced image is reconstructed via inverse DWT. Experiments on real-world sluice gate datasets demonstrate that WWSF outperforms six state-of-the-art methods, achieving the highest scores on UIQM and AG while remaining competitive on entropy (EN). Moreover, the method retains strong robustness under high turbidity conditions (T ≥ 35 NTU), producing sharper edges, more faithful color representation, and improved texture clarity. These results indicate that WWSF is an effective preprocessing tool for downstream tasks such as segmentation, defect classification, and condition assessment of hydraulic infrastructure in complex underwater environments. Full article

Hydraulic concrete is a critical infrastructure material, with its surface condition playing a vital role in quality assessments for water conservancy and hydropower projects. However, images taken in complex hydraulic environments often suffer from degraded quality due to low lighting, shadows, and noise, making it difficult to distinguish defects from the background and thereby hindering accurate defect detection and damage evaluation. In this study, following systematic analyses of hydraulic concrete color space characteristics, we propose a Dual-Branch Luminance–Chrominance Attention Network (DBLCANet-HCIE) specifically designed for low-light hydraulic concrete image enhancement. Inspired by human visual perception, the network simultaneously improves global contrast and preserves fine-grained defect textures, which are essential for structural analysis. The proposed architecture consists of a Luminance Adjustment Branch (LAB) and a Chroma Restoration Branch (CRB). The LAB incorporates a Luminance-Aware Hybrid Attention Block (LAHAB) to capture both the global luminance distribution and local texture details, enabling adaptive illumination correction through comprehensive scene understanding. The CRB integrates a Channel Denoiser Block (CDB) for channel-specific noise suppression and a Frequency-Domain Detail Enhancement Block (FDDEB) to refine chrominance information and enhance subtle defect textures. A feature fusion block is designed to fuse and learn the features of the outputs from the two branches, resulting in images with enhanced luminance, reduced noise, and preserved surface anomalies. To validate the proposed approach, we construct a dedicated low-light hydraulic concrete image dataset (LLHCID). Extensive experiments conducted on both LOLv1 and LLHCID benchmarks demonstrate that the proposed method significantly enhances the visual interpretability of hydraulic concrete surfaces while effectively addressing low-light degradation challenges. Full article

See-through augmented reality glasses, such as HoloLens 2, are increasingly adopted in medical settings; however, their efficacy as medical display devices remains unclear, as current evaluation protocols are designed for traditional monitors. This study examined whether the established display-evaluation techniques apply to HoloLens 2 and whether it meets standards for primary and secondary medical displays. HoloLens 2 was assessed for overall image quality, luminance, grayscale consistency, and color uniformity. Five participants rated the TG18-OIQ pattern under ambient lighting conditions of 2.4 and 138.7 lx. Minimum and maximum luminance were measured using the TG18-LN12-03 and -18 patterns, targeting ≥ 300 cd/m² and a luminance ratio ≥ 250. Grayscale conformity to the standard grayscale display function allowed deviations of 10% for primary and 20% for secondary displays. Color uniformity was measured at five screen positions for red, green, and blue, with a chromaticity limit of 0.01 for primary displays. HoloLens 2 satisfied four of the ten primary and four of the seven secondary overall-quality criteria, achieving a maximum luminance of 2366 cd/m² and a luminance ratio of 1478.75. Grayscale uniformity was within tolerance for 10 of the 15 primary and 13 of the 15 secondary measurements, while 25 of the 30 color uniformity values exceeded the threshold. The adapted evaluation methods facilitate a systematic assessment of HoloLens 2 as a medical display. Owing to inadequate grayscale and color representation, the headset is unsuitable as a primary diagnostic display; for secondary use, requirements must be assessed based on specific application requirements. Full article

For large oil-immersed transformers, their metal-enclosed structure poses significant challenges for direct visual inspection of internal defects. To ensure the effective detection of internal insulation defects, this study employs a self-developed micro-robot for internal visual inspection. Given the substantial morphological and dimensional variations of target defects (e.g., carbon traces produced by surface discharge inside the transformer), the intelligent and efficient extraction of carbon trace features from complex backgrounds becomes critical for robotic inspection. To address these challenges, we propose the DCMC-UNet, a semantic segmentation model for carbon traces containing adaptive illumination enhancement and dynamic feature fusion. For blurred carbon trace images caused by unstable light reflection and illumination in transformer oil, an improved CLAHE algorithm is developed, incorporating learnable parameters to balance luminance and contrast while enhancing edge features of carbon traces. To handle the morphological diversity and edge complexity of carbon traces, a dynamic deformable encoder (DDE) was integrated into the encoder, leveraging deformable convolutional kernels to improve carbon trace feature extraction. An edge-aware decoder (EAD) was integrated into the decoder, which extracts edge details from predicted segmentation maps and fuses them with encoded features to enrich edge features. To mitigate the semantic gap between the encoder and the decoder, we replace the standard skip connection with a cross-level attention connection fusion layer (CLFC), enhancing the multi-scale fusion of morphological and edge features. Furthermore, a multi-scale atrous feature aggregation module (MAFA) is designed in the neck to enhance the integration of deep semantic and shallow visual features, improving multi-dimensional feature fusion. Experimental results demonstrate that DCMC-UNet outperforms U-Net, U-Net++, and other benchmarks in carbon trace segmentation. For the transformer carbon trace dataset, it achieves better segmentation than the baseline U-Net, with an improved mIoU of 14.04%, Dice of 10.87%, pixel accuracy (P) of 10.97%, and overall accuracy (Acc) of 5.77%. The proposed model provides reliable technical support for surface discharge intensity assessment and insulation condition evaluation in oil-immersed transformers. Full article

Low-light image enhancement remains a challenging task in computer vision due to the complex interplay of noise, asymmetrical artifacts, illumination non-uniformity, and detail preservation. Existing methods such as traditional histogram equalization, gamma correction, and Retinex-based approaches often struggle to balance contrast improvement and naturalness preservation. Deep learning methods such as CNNs and transformers have shown promise, but face limitations in modeling multi-scale illumination and long-range dependencies. To address these issues, we propose WIGformer, a novel wavelet-based illumination-guided transformer framework for low-light image enhancement. The proposed method extends the single-stage Retinex theory to explicitly model noise in both reflectance and illumination components. It introduces a wavelet illumination estimator with a Wavelet Feature Enhancement Convolution (WFEConv) module to capture multi-scale illumination features and an illumination feature-guided corruption restorer with an Illumination-Guided Enhanced Multihead Self-Attention (IGEMSA) mechanism. WIGformer leverages the symmetry properties of wavelet transforms to achieve multi-scale illumination estimation, ensuring balanced feature extraction across different frequency bands. The IGEMSA mechanism integrates adaptive feature refinement and illumination guidance to suppress noise and artifacts while preserving fine details. The same mechanism allows us to further exploit symmetrical dependencies between illumination and reflectance components, enabling robust and natural enhancement of low-light images. Extensive experiments on the LOL-V1, LOL-V2-Real, and LOL-V2-Synthetic datasets demonstrate that WIGformer achieves state-of-the-art performance and outperforms existing methods, with PSNR improvements of up to 26.12 dB and an SSIM score of 0.935. The qualitative results demonstrate WIGformer’s superior capability to not only restore natural illumination but also maintain structural symmetry in challenging conditions, preserving balanced luminance distributions and geometric regularities that are characteristic of properly exposed natural scenes. Full article

In this paper, a two-stage framework is proposed for the energy management of microgrids, which combines a hybrid Convolutional Neural Network-Gated Recurrent Unit (CNN-GRU) forecast model and the Improved Teaching–Learning-Based Optimization (ITLBO) algorithm. The CNN-GRU model captures spatiotemporal patterns in historical data for effective renewable energy and load demand uncertainty quantification, while the ITLBO algorithm improves generation scheduling performance through utilization of adaptive luminance coefficients, Latin Hypercube initialization, and hybrid genetic operations. The proposed framework is then compared with four different forecasting models: standalone CNN or MLANN, and three popular optimization algorithms (PSO, TLBO, CO) for four cases, including baseline (perfect foresight), CNN-GRU forecast, CNN forecast, and MLANN forecast. The results show that the hybrid framework outperforms dedicated, in-domain models for forecast and scheduling, with the state-of-the-art CNN-GRU sliding window model producing the best forecasting accuracy, which subsequently translates into near-optimal scheduling performance. Through many experiments, we show that the ITLBO algorithm is robust and outperforms the classical optimization methods on convergence speed and solution quality while significantly eliminating the forecast errors uncertainty. Demand response is also a feature of these models, which boosts operational efficiency by scaling down peak grid usage without sacrificing affordability through energy saving capabilities. According to the results, the hybrid framework exhibits significant cost-efficiency by reducing the RMSE of solar irradiance forecasting by 11.6% when compared to standalone CNN and achieving a 69.7% reduction in operational costs under ITLBO optimization. The comparative analysis emphasizes the robustness and versatility of the framework, reinforcing its feasibility across a range of forecasting and optimization scenarios for real-world microgrid deployment. Full article

Automatic surveillance systems have become essential tools for urban centers. These technologies enable intelligent monitoring that is both versatile and non-intrusive. Today, these systems can analyze various aspects, such as urban traffic, citizen behavior, and the detection of unusual activities. Most intelligent systems utilize photo sensors to gather information and assess situations. They analyze data sequences from these photo sensors over time to detect moving objects or other relevant information. In this context, background modeling approaches are crucial for efficiently detecting moving objects by differentiating between the foreground and background, which serves as the basis for further analysis. Although current methods are effective, the dynamic nature of outdoor environments can limit their performance due to numerous external variables that affect the collected information. This paper introduces a novel algorithm that uses mathematical morphology to create a background model by analyzing texture information in discrete spaces, leading to an efficient solution for the background subtraction task. The algorithm dynamically adjusts to global luminance conditions and effectively distinguishes texture information to label the foreground and background using morphological filters. A key advantage of this approach is its use of discrete working spaces, which enables faster implementation on standard hardware, making it suitable for a variety of devices. Finally, our proposal is tested against reference datasets of surveillance and common background subtraction algorithms, demonstrating that our method adapts better to outdoor conditions, making it more robust in detecting different moving objects. Full article

Constant luminous flux lamps are required for ensuring reliable and consistent illumination in various applications, including emergency lighting, outdoor activities, and general use. However, some activities may require maintaining a constant luminous flux, where the design must control the current during the use. This paper presents the design of a portable light-emitting diode (LED) lighting system powered by batteries that maintains constant luminous flux using the zero-average dynamic control (ZAD) and a proportional-integral-derivative (PID) controllers. This system can adapt the current to maintain the luminous flux required for reliable portable lighting applications used in outdoor activities. The results show that the system can provide constant illumination with 12-volt, 18-volt, and 24-volt batteries, and a 12-volt battery with a state of charge of 10%, enhancing usability for outdoor activities, emergency situations, and professional applications. Full article

The development of black tea quality is the outcome of the synergistic interaction between tea cultivars and the ecological environment of the production area, including factors such as climate, soil, and cultivation practices. Nevertheless, within a specific geographical region, systematic analysis of the environmental regulation mechanisms governing processing adaptability and quality formation among different cultivars remains insufficient. This study evaluated six Camellia sinensis cultivars from the Jingshan region of Hangzhou, China, integrating non-targeted metabolomics, sensory profiling, bioassays, and molecular docking to elucidate cultivar-specific quality attributes. Non-volatile metabolomics identified 84 metabolites linked to color and taste, including amino acids, catechins, flavonoid glycosides, and phenolic acids. Sensory and metabolite correlations revealed that amino acids enhanced brightness and imparted fresh-sweet flavors, while catechins contributed to bitterness and astringency. Specific metabolites, such as 4-hydroxybenzoyl glucose and feruloyl quinic acid, modulated color luminance. Volatile analysis identified 13 aroma-active compounds (OAV ≥ 1), with 1-octen-3-ol, phenylacetaldehyde, and linalool endowing JK with distinct floral-fruity notes. Molecular docking further demonstrated interactions between these volatiles and olfactory receptors (e.g., OR1A1 and OR2J2), providing mechanistic insights into aroma perception. These findings establish a robust link between cultivar-driven metabolic profiles in black tea, offering actionable criteria for cultivar selection and quality optimization in regional tea production. Full article

Existing low-light image enhancement methods often struggle to effectively enhance space targets in deep-space contexts due to the effects of extremely low illumination, stellar stray light, and Earth halos. This work proposes a low-light image enhancement method based on multi-image fusion, which integrates features of space targets with the Retinex theory. The method dynamically adjusts contrast by detecting luminance distribution and incorporates an adaptive noise removal mechanism for enhanced image quality. This method effectively balances detail enhancement with noise suppression. This work presents experiments on deep-space background images featuring 10 types of artificial satellites, including AcrimSat, Calipso, Jason, and others. Experimental results demonstrate that the proposed method outperforms traditional methods and mainstream deep learning models in qualitative and quantitative evaluations, particularly in suppressing Earth halo interference. This study establishes an effective framework for improving the visual quality of spacecraft images and provides important technical support for applications such as spacecraft identification, space target detection, and autonomous spacecraft navigation. Full article

Qinghai Province urgently requires the development of adaptive energy-efficient rural housing construction to address resettlement needs arising from hydropower projects, given the region’s characteristic combination of high solar irradiance resources and severe cold climate conditions. This research establishes localized retrofit strategies through systematic field investigations and Rhinoceros modeling simulations of five representative rural residences across four villages. The key findings reveal that comprehensive building envelope retrofits achieve an 80% reduction in energy consumption. South-facing sunspaces demonstrate effective thermal buffering capacity, though their spatial depth exhibits negligible correlation with heating energy requirements. An optimized hybrid shading system combining roof overhangs and vertical louvers demonstrates critical efficacy in summer overheating mitigation, with vertical louvers demonstrating superior thermal and luminous regulation precision. Architectural orientation analysis identifies an optimal alignment within ±10° of true south, emphasizing the functional zoning principle of positioning primary living spaces in south-oriented ground floor areas while locating auxiliary functions in northeastern/northwestern zones. The integrated design framework synergizes three core components: passive solar optimization, climate-responsive shading mechanisms, and performance-enhanced envelope systems, achieving simultaneous improvements in energy efficiency and thermal comfort within resettlement housing constraints. This methodology establishes a replicable paradigm for climate-resilient rural architecture in high-altitude, solar-intensive cold regions, effectively reconciling community reconstruction needs with low-carbon development imperatives through context-specific technical solutions. Full article

This is the studyto investigate the correlation between macular cone density (MCD) and flicker electroretinogram (ERG) response in healthy eyes. In this exploratory study, 23 eyes from 12 healthy subjects were enrolled in this study. The fixed-luminance flicker full-field electroretinogram (ffERG) responses of the retina and MCDs at 24 locations were measured using the Diopsys^® NOVA™ system and the rtx1 adaptive optics retinal camera, respectively. Regression analysis was employed to evaluate the correlations. The mean age of the subjects was 30 ± 3 years. The average magnitudes of the flicker response and phase response were 13.44 ± 4.88 μV and 332.63 ± 22.12°, respectively. The MCDs for all 24 locations were 15,043 ± 3511 cones/mm². Among all locations, regression analysis revealed a significant correlation only at one specific location (0, −4°) between cone density and both the mean magnitude and phase of the flicker response, with p-values of 0.005 and 0.004, respectively.In conclusion, we identified a significant correlation between MCD and ffERG responses at a specific retinal locus (0, −4°). This finding may be attributed to the distribution of different cone types throughout the retina and the possibility that various cone types may contribute differently to ERG. Further studies are required to investigate this finding. Full article

Surface defects in magnetic tiles pose significant challenges to the performance and reliability of permanent magnet motors. Traditional defect detection methods, including visual inspection and 2D imaging, are limited by subjectivity, resolution constraints, and a lack of depth information, making precise defect quantification challenging. To address this challenge, this study explores a defect detection and quantitative evaluation framework based on high-resolution 3D laser scanning technology. Our approach integrates point cloud acquisition with luminance and point cloud mapping (LPM) to enhance defect visualization. Furthermore, we introduce an adaptive neighborhood selection method based on information entropy, enabling accurate normal vector and curvature estimation while reducing reliance on manual parameter tuning. Even when the point cloud density decreases to 40%, the mean estimation error and root-mean-square error remain within 3°. By leveraging single-frame and multi-frame point cloud fitting, our method transitions from coarse defect extraction to fine refinement, enhancing detection precision. To further improve accuracy and minimize false negatives, we apply region-growing techniques for defect region completion. Experimental results indicate that our method can reliably detect surface defects as small as 0.07 mm², achieving an average precision of 93.91%, a recall of 95.97%, and an F1 of 94.91%. Compared to conventional 2D image-based methods, our method offers superior defect quantification, lower computational costs, and minimal hardware requirements, making it highly suitable for real-time online defect detection in industrial applications. Full article