Search Results (1,681)

The fusion of infrared and visible light images is a crucial technology for enhancing visual perception in complex environments. It plays a pivotal role in improving visual perception and subsequent performance in advanced visual tasks. However, due to the significant degradation of visible light image quality in low-light or nighttime scenes, most existing fusion methods often struggle to obtain sufficient texture details and salient features when processing such scenes. This can lead to a decrease in fusion quality. To address this issue, this article proposes a new image fusion method called BMFusion. Its aim is to significantly improve the quality of fused images in low-light or nighttime scenes and generate high-quality fused images around the clock. This article first designs a brightness attention module composed of brightness attention units. It extracts multimodal features by combining the SimAm attention mechanism with a Transformer architecture. Effective enhancement of brightness and features has been achieved, with gradual brightness attention performed during feature extraction. Secondly, a complementary fusion module was designed. This module deeply fuses infrared and visible light features to ensure the complementarity and enhancement of each modal feature during the fusion process, minimizing information loss to the greatest extent possible. In addition, a feature reconstruction network combining CLIP-guided semantic vectors and neighborhood attention enhancement was proposed in the feature reconstruction stage. It uses the KAN module to perform channel adaptive optimization on the reconstruction process, ensuring semantic consistency and detail integrity of the fused image during the reconstruction phase. The experimental results on a large number of public datasets demonstrate that the BMFusion method can generate fusion images with higher visual quality and richer details in night and low-light environments compared with various existing state-of-the-art (SOTA) algorithms. At the same time, the fusion image can significantly improve the performance of advanced visual tasks. This shows the great potential and application prospect of this method in the field of multimodal image fusion. Full article

This paper proposes a novel polar coding scheme tailored for indoor visible light communication (VLC) systems. Simulation results demonstrate a significant reduction in bit error rate (BER) compared to uncoded transmission, with a coding gain of at least 5 dB. Furthermore, the reliable communication area of the VLC system is substantially extended. Building on this foundation, this study explores the joint design of polar codes and physical-layer network coding (PNC) for VLC systems. Simulation results illustrate that the BER of our scheme closely approaches that of the conventional VLC relay scheme. Moreover, our approach doubles the throughput, cuts equipment expenses in half, and boosts effective bit rates per unit time-slot twofold. This proposed design noticeably advances the performance of VLC systems and is particularly well-suited for scenarios with low-latency demands. Full article

Integrating infrared and visible-light images facilitates a more comprehensive understanding of scenes by amalgamating dual-sensor data derived from identical environments. Traditional CNN-based fusion techniques are predominantly confined to local feature emphasis due to their inherently limited receptive fields. Conversely, Transformer-based models tend to prioritize global information, which can lead to a deficiency in feature diversity and detail retention. Furthermore, methods reliant on single-scale feature extraction are inadequate for capturing extensive scene information. To address these limitations, this study presents GLMAFuse, an innovative dual-stream encoder–decoder network, which utilizes a multi-scale attention mechanism to harmoniously integrate global and local features. This framework is designed to maximize the extraction of multi-scale features from source images while effectively synthesizing local and global information across all layers. We introduce the global-aware and local embedding (GALE) module to adeptly capture and merge global structural attributes and localized details from infrared and visible imagery via a parallel dual-branch architecture. Additionally, the multi-scale attention fusion (MSAF) module is engineered to optimize attention weights at the channel level, facilitating an enhanced synergy between high-frequency edge details and global backgrounds. This promotes effective interaction and fusion of dual-modal features. Extensive evaluations using standard datasets demonstrate that GLMAFuse surpasses the existing leading methods in both qualitative and quantitative assessments, highlighting its superior capability in infrared and visible image fusion. On the TNO and MSRS datasets, our method achieves outstanding performance across multiple metrics, including EN (7.15, 6.75), SD (46.72, 47.55), SF (12.79, 12.56), MI (2.21, 3.22), SCD (1.75, 1.80), VIF (0.79, 1.08), Qbaf (0.58, 0.71), and SSIM (0.99, 1.00). These results underscore its exceptional proficiency in infrared and visible image fusion. Full article

Fusarium wilt of banana is a soil-borne vascular disease caused by Fusarium oxysporum f. sp. cubense. The rapid and accurate detection of this disease is of great significance to controlling its spread. The research objective was to explore rapid banana Fusarium wilt latency and onset detection methods and establish a disease severity grading model. Visible/near-infrared spectroscopy analysis combined with machine learning methods were used for the rapid in vivo detection of banana Fusarium wilt. A portable visible/near-infrared spectrum acquisition system was constructed to collect the spectra data of banana Fusarium wilt leaves representing five different disease grades, totaling 106 leaf samples which were randomly divided into a training set with 80 samples and a test set with 26 samples. Different data preprocessing methods were utilized, and Fisher discriminant analysis (FDA), an extreme learning machine (ELM), and a one-dimensional convolutional neural network (1D-CNN) were used to establish the classification models of the disease grades. The classification accuracies of the FDA, ELM, and 1D-CNN models reached 0.891, 0.989, and 0.904, respectively. The results showed that the proposed visible/near infrared spectroscopy detection method could realize the detection of the incubation period of banana Fusarium wilt and the classification of the disease severity and could be a favorable tool for the field diagnosis of banana Fusarium wilt. Full article

Global warming and extreme climate conditions caused by unsuitable temperature and humidity lead to coffee leaf rust (Hemileia vastatrix) diseases in coffee plantations. Coffee leaf rust is a severe problem that reduces productivity. Currently, pesticide spraying is considered the most effective solution for mitigating coffee leaf rust. However, the application of pesticide spray is still not efficient for most farmers worldwide. In these cases, pruning the most infected leaves with leaf rust at coffee plantations is important to help pesticide spraying to be more efficient by creating a more targeted, accessible treatment. Therefore, detecting coffee leaf rust is important to support the decision on pruning infected leaves. The dataset was acquired from a coffee farm in Majalengka Regency, Indonesia. Only images with clearly visible spots of coffee leaf rust were selected. Data collection was performed via two devices, a digital mirrorless camera and a phone camera, to diversify the dataset and test it with different datasets. The dataset, comprising a total of 2024 images, was divided into three sets with a ratio of 70% for training (1417 images), 20% for validation (405 images), and 10% for testing (202 images). Images with leaves infected by coffee leaf rust were labeled via LabelImg^® with the label “CLR”. All labeled images were used to train the YOLOv5 and YOLOv8 algorithms through the convolutional neural network (CNN). The trained model was tested with a test dataset, a digital mirrorless camera image dataset (100 images), a phone camera dataset (100 images), and real-time detection with a coffee leaf rust image dataset. After the model was trained, coffee leaf rust was detected in each frame. The mean average precision (mAP) and recall for the trained YOLOv5 model were 69% and 63.4%, respectively. For YOLOv8, the mAP and recall were approximately 70.2% and 65.9%, respectively. To evaluate the performance of the two trained models in detecting coffee leaf rust on trees, 202 original images were used for testing with the best-trained weight from each model. Compared to YOLOv5, YOLOv8 demonstrated superior accuracy in detecting coffee leaf rust. With a mAP of 73.2%, YOLOv8 outperformed YOLOv5, which achieved a mAP of 70.5%. An edge device was utilized to deploy real-time detection of CLR with the best-trained model. The detection was successfully executed with high confidence in detecting CLR. The system was further integrated into pruning solutions for Arabica coffee farms. A pruning device was designed using Autodesk Fusion 360^® and fabricated for testing on a coffee plantation in Indonesia. Full article

Soluble solids content (SSC) measurements are crucial for managing longan production and post-harvest handling. However, most traditional SSC detection methods are destructive, cumbersome, and unsuitable for field applications. This study proposes a novel field detection model (Brix-back propagation neural network, Brix-BPNN), designed for longan SSC grading based on an improved BP neural network. Initially, nine preprocessing methods were combined with six classification algorithms to develop the longan SSC grading prediction model. Among these, the model preprocessed with Savitzky–Golay smoothing and the first derivative (SG-D1) demonstrated a 7.02% improvement in accuracy compared to the original spectral model. Subsequently, the BP network structure was refined, and the competitive adaptive reweighted sampling (CARS) algorithm was employed for feature wavelength extraction. The results show that the improved Brix-BPNN model, integrated with the CARS, achieves the highest prediction performance, with a 2.84% increase in classification accuracy relative to the original BPNN model. Additionally, the number of wavelengths is reduced by 92% compared to the full spectrum, making this model both lightweight and efficient for rapid field detection. Furthermore, a portable detection device based on visible-near-infrared (Vis-NIR) spectroscopy was developed for longan SSC grading, achieving a prediction accuracy of 83.33% and enabling fast, nondestructive testing in field conditions. Full article

In recent years, the morphology control of semiconductor nanomaterials has been attracting increasing attention toward maximizing their functional properties and reaching their end use in real-world devices. However, the development of easy and cost-effective methods for preparing large-scale patterned semiconductor structures on flexible temperature-sensitive substrates remains ever in demand. In this study, vapor post-treatment (VPT) is investigated as a potential, simple and low-cost post-preparative method to morphologically modify gravure-printed zinc oxide (ZnO) nanoparticulate thin films at low temperatures. Exposing nanoparticles (NPs) to acidic vapor solution, spontaneous restructuring pathways are observed as a consequence of NPs tending to reduce their high interfacial energy. Depending on the imposed environmental conditions during the treatment (e.g., temperature, vapor composition), various ZnO thin-film morphologies are produced, from dense to porous ones, as a result of the activation and interplay of different spontaneous interface elimination mechanisms, including dissolution–precipitation, grain boundary migration and grain rotation–coalescence. The influence of VPT on structural/optical properties has been examined via XRD, UV–visible and photoluminescence measurements. Controlling NP junctions and network nanoporosity, VPT appears as promising cost-effective, low-temperature and pressureless post-preparative platform for preparing supported ZnO NP-based films with improved connectivity and mechanical stability, favoring their practical use and integration in flexible devices. Full article

Since conventional photogrammetric approaches struggle with with low-texture, reflective, and transparent regions, this study explores the application of Neural Radiance Fields (NeRFs) for large-scale 3D reconstruction of outdoor scenes, since NeRF-based methods have recently shown very impressive results in these areas. We evaluate three approaches: Mega-NeRF, Block-NeRF, and Direct Voxel Grid Optimization, focusing on their accuracy and completeness compared to ground truth point clouds. In addition, we analyze the effects of using multiple sub-modules, estimating the visibility by an additional neural network and varying the density threshold for the extraction of the point cloud. For performance evaluation, we use benchmark datasets that correspond to the setting off standard flight campaigns and therefore typically have nadir camera perspective and relatively little image overlap, which can be challenging for NeRF-based approaches that are typically trained with significantly more images and varying camera angles. We show that despite lower quality compared to classic photogrammetric approaches, NeRF-based reconstructions provide visually convincing results in challenging areas. Furthermore, our study shows that in particular increasing the number of sub-modules and predicting the visibility using an additional neural network improves the quality of the resulting reconstructions significantly. Full article

Data augmentation methods offer a cost-effective and efficient alternative to the acquisition of additional data, significantly enhancing data diversity and model generalization, making them particularly favored in object detection tasks. However, existing data augmentation techniques primarily focus on the visible spectrum and are directly applied to RGB-T object detection tasks, overlooking the inherent differences in image data between the two tasks. Visible images capture rich color and texture information during the daytime, while infrared images are capable of imaging under low-light complex scenarios during the nighttime. By integrating image information from both modalities, their complementary characteristics can be exploited to improve the overall effectiveness of data augmentation methods. To address this, we propose a cross-modality data augmentation method tailored for RGB-T object detection, leveraging masked image modeling within representation learning. Specifically, we focus on the temporal consistency of infrared images and combine them with visible images under varying lighting conditions for joint data augmentation, thereby enhancing the realism of the augmented images. Utilizing the masked image modeling method, we reconstruct images by integrating multimodal features, achieving cross-modality data augmentation in feature space. Additionally, we investigate the differences and complementarities between data augmentation methods in data space and feature space. Building upon existing theoretical foundations, we propose an integrative framework that combines these methods for improved augmentation effectiveness. Furthermore, we address the slow convergence observed with the existing Mosaic method in aerial imagery by introducing a multi-scale training strategy and proposing a full-scale Mosaic method as a complement. This optimization significantly accelerates network convergence. The experimental results validate the effectiveness of our proposed method and highlight its potential for further advancements in cross-modality object detection tasks. Full article

Rolling bearings are vital components in rotating machinery, and their reliable operation is crucial for maintaining the stability and efficiency of mechanical systems. However, fault detection in rolling bearings is often hindered by noise interference in complex industrial environments. To overcome this challenge, this paper presents a novel fault diagnosis method for rolling bearings, combining Convolutional Neural Networks (CNNs) and Gated Recurrent Units (GRUs), integrated with the envelope analysis and adaptive mean filtering techniques. Initially, envelope analysis and adaptive mean filtering are applied to suppress random noise in the bearing signals, thereby enhancing the visibility of fault features. Subsequently, a deep learning model that combines a CNN and a GRU is developed: the CNN extracts spatial features, while the GRU captures the temporal dependencies between these features. The integration of the CNN and GRU significantly improves the accuracy and robustness of fault diagnosis. The proposed method is validated using the CWRU dataset, with the experimental results achieving an average accuracy of 99.25%. Additionally, the method is compared to four classical fault diagnosis models, demonstrating superior performance in terms of both diagnostic accuracy and generalization ability. The results, supported by various visualization techniques, show that the proposed approach effectively addresses the challenges of fault detection in rolling bearings under complex industrial conditions. Full article

Wave breaking is a fundamental process in ocean energy dissipation and plays a crucial role in the exchange between ocean and nearshore sediments. Foam, the primary visible feature of wave breaking areas, serves as a direct indicator of wave breaking processes. Monitoring the distribution of foam via remote sensing can reveal the spatiotemporal patterns of nearshore wave breaking. Existing studies on wave breaking processes primarily focus on individual wave events or short timescales, limiting their effectiveness for nearshore regions where hydrodynamic processes are often represented at tidal cycles. In this study, video imagery from a typical low-tide terrace (LTT) beach was segmented into four categories, including the wave breaking foam, using the DeepLabv3+ architecture, a convolutional neural networks (CNNs)-based model suitable for semantic segmentation in complex visual scenes. After training and testing on a manually labelled dataset, which was divided into training, validation, and testing sets based on different time periods, the overall classification accuracy of the model was 96.4%, with an accuracy of 96.2% for detecting wave breaking foam. Subsequently, a heatmap of the wave breaking foam distribution over a tidal cycle on the LTT beach was generated. During the tidal cycle, the foam distribution density exhibited both alongshore variability, and a pronounced bimodal structure in the cross-shore direction. Analysis of morphodynamical data collected in the field indicated that the bimodal structure is primarily driven by tidal variations. The wave breaking process is a key factor in shaping the profile morphology of LTT beaches. High-frequency video monitoring further showed the wave breaking patterns vary significantly with tidal levels, leading to diverse geomorphological features at various cross-shore locations. Full article

In this study, novel anion photo-responsive supramolecular hydrogels based on cysteine–silver sol (CSS) and iodate anions (IO₃⁻) were prepared. The peculiarities of the self-assembly process of gel formation in the dark and under visible-light exposure were studied using a complex of modern physico-chemical methods of analysis, including viscosimetry, UV spectroscopy, dynamic light scattering, electrophoretic light scattering, scanning electron microscopy, energy-dispersive X-ray spectroscopy, and X-ray photoelectron spectroscopy. In the dark phase, the formation of weak snot-like gels takes place in a quite narrow IO₃⁻ ion concentration range. The visible-light exposure of these gels leads to an increase in their viscosity and dramatic change in their color. The morphology of gels alters after light irradiation that is reflected in the formation of a huge number of spherical/elliptical particles and the thickening of the fibers of the gel network. The interaction of CSS with IO₃⁻ anions has features of a redox process, which leads to the formation of silver iodide/silver oxide nanoparticles inside and on the surface of CSS particles. CSS possesses selectivity only to IO₃⁻ anions compared to many other inorganic ions relevant for humans and the environment. Thus, the CSS/IO₃⁻ system is non-trivial and can be considered as a novel low-molecular-weight gelator with photosensitive properties, as another way to produce silver iodide nanoparticles, and as a new approach for IO₃⁻ ion detection. Full article

Aiming at the problem of high false detection and missed detection rate of apple surface defects in complex environments, a new apple surface defect detection network: space-to-depth convolution-Multi-scale Empty Attention-Context Guided Feature Pyramid Network-You Only Look Once version 8 nano (SMC-YOLOv8n) is designed. Firstly, space-to-depth convolution (SPD-Conv) is introduced before each Faster Implementation of CSP Bottleneck with 2 convolutions (C2f) in the backbone network as a preprocessing step to improve the quality of input data. Secondly, the Bottleneck in C2f is removed in the neck, and Multi-scale Empty Attention (MSDA) is introduced to enhance the feature extraction ability. Finally, the Context Guided Feature Pyramid Network (CGFPN) is used to replace the Concat method of the neck for feature fusion, thereby improving the expression ability of the features. Compared with the YOLOv8n baseline network, mean Average Precision (mAP) 50 increased by 2.7% and 1.1%, respectively, and mAP50-95 increased by 4.1% and 2.7%, respectively, on the visible light apple surface defect data set and public data set in the self-made complex environments.The experimental results show that SMC-YOLOv8n shows higher efficiency in apple defect detection, which lays a solid foundation for intelligent picking and grading of apples. Full article

Metalens can achieve arbitrary light modulation by controlling the amplitude, phase, and polarization of the incident waves and have been applied across various fields. This paper presents a color router designed based on metalens, capable of effectively separating spectra from visible light to near-infrared light. Traditional design methods for meta-lenses require extensive simulations, making them time-consuming. In this study, we propose a deep learning network capable of forward prediction across a broad wavelength range, combined with a particle swarm optimization algorithm to design metalens efficiently. The simulation results align closely with theoretical predictions. The designed color router can simultaneously meet the theoretical transmission phase of the target spectra, specifically for red, green, blue, and near-infrared light, and focus them into designated areas. Notably, the optical efficiency of this design reaches 40%, significantly surpassing the efficiency of traditional color filters. Full article

Vehicle-to-vehicle (V2V) visible light communication (VLC) systems are increasingly being deployed for real-time data exchange in intelligent transportation systems (ITS). However, these systems are highly vulnerable to eavesdropping, especially in scenarios such as road intersections where signals may be exposed to unauthorized receivers. To address these security challenges, we propose a novel reconfigurable intelligent surface (RIS)-assisted security enhancement scheme for V2V VLC networks. The proposed scheme leverages RIS to improve the reception of legitimate signals at the destination vehicle while simultaneously introducing artificial noise (AN) to interfere with potential eavesdroppers. Optimization problems are formulated to maximize the SINR of the destination vehicle and simultaneously minimize the worst-case SINR of eavesdroppers. The simulation results demonstrate that the proposed scheme achieves a notable improvement in the system’s secrecy rate by 1.64 bit/s/Hz and enhances the overall security performance, offering a robust solution to the security challenges in V2V VLC systems. Full article