Search Results (28,281)

This paper presents an in-depth study of the automated recognition and geometric information quantification of rebar meshes, proposing a deep learning-based method for rebar mesh detection and segmentation. By constructing a diverse rebar mesh image dataset, an improved Unet-based model was developed, incorporating residual modules to enhance the network’s feature extraction capabilities and training efficiency. The study found that the improved model maintains high segmentation accuracy and robustness even in the presence of complex backgrounds and noise. To achieve the precise measurement of rebar spacing, a rebar intersection detection algorithm based on convolution operations was designed, and the IQR (Interquartile Range) algorithm was applied to remove outliers, ensuring the accuracy and reliability of spacing calculations. The experimental results demonstrate that the proposed model and methods effectively and efficiently accomplish the automated recognition and geometric information extraction of rebar meshes, providing reliable technical support for the automated detection and geometric data analysis of rebar meshes in practical engineering applications. Full article

Underwater pile foundation detection is crucial for environmental monitoring and marine engineering. Traditional methods for detecting underwater pile foundations are labor-intensive and inefficient. Deep learning-based image processing has revolutionized detection, enabling identification through sonar imagery analysis. This study proposes an innovative methodology, named the AquaPile-YOLO algorithm, for underwater pile foundation detection. Our approach significantly enhances detection accuracy and robustness by integrating multi-scale feature fusion, improved attention mechanisms, and advanced data augmentation techniques. Trained on 4000 sonar images, the model excels in delineating pile structures and effectively identifying underwater targets. Experimental data show that the model can achieve good target identification results in similar experimental scenarios, with a 96.89% accuracy rate for underwater target recognition. Full article

Nanotheranostics integrates diagnostic and therapeutic functionalities using nanoscale materials, advancing personalized medicine by enhancing treatment precision and reducing adverse effects. Key materials for nanotheranostics include metallic nanoparticles, quantum dots, carbon dots, lipid nanoparticles and polymer-based nanocarriers, each offering unique benefits alongside specific challenges. Polymer-based nanocarriers, including hybrid and superparamagnetic nanoparticles, improve stability and functionality but are complex to manufacture. Polymeric nanoparticles with aggregation-induced emission (AIE) present promising theranostic potential for cancer detection and treatment. However, challenges such as translating the AIE concept to living systems, addressing toxicity concerns, overcoming deep-tissue imaging limitations, or ensuring biocompatibility remain to be resolved. Recently, cluster-triggered emission (CTE) polymers have emerged as innovative materials in nanotheranostics, offering enhanced fluorescence and biocompatibility. These polymers exhibit increased fluorescence intensity upon aggregation, making them highly sensitive for imaging and therapeutic applications. CTE nanoparticles, crafted from biodegradable polymers, represent a safer alternative to traditional nanotheranostics that rely on embedding conventional fluorophores or metal-based agents. This advancement significantly reduces potential toxicity while enhancing biocompatibility. The intrinsic fluorescence allows real-time monitoring of drug distribution and activity, optimizing therapeutic efficacy. Despite their potential, these systems face challenges such as maintaining stability under physiological conditions and addressing the need for comprehensive safety and efficacy studies to meet clinical and regulatory standards. Nevertheless, their unique properties position CTE nanoparticles as promising candidates for advancing theranostic strategies in personalized medicine, bridging diagnostic and therapeutic functionalities in innovative ways. Full article

Registering light detection and ranging (LiDAR) data with optical camera images enhances spatial awareness in autonomous driving, robotics, and geographic information systems. The current challenges in this field involve aligning 2D-3D data acquired from sources with distinct coordinate systems, orientations, and resolutions. This paper introduces a new pipeline for camera–LiDAR post-registration to produce colorized point clouds. Utilizing deep learning-based matching between 2D spherical projection LiDAR feature layers and camera images, we can map 3D LiDAR coordinates to image grey values. Various LiDAR feature layers, including intensity, bearing angle, depth, and different weighted combinations, are used to find correspondence with camera images utilizing state-of-the-art deep learning matching algorithms, i.e., SuperGlue and LoFTR. Registration is achieved using collinearity equations and RANSAC to remove false matches. The pipeline’s accuracy is tested using survey-grade terrestrial datasets from the TX5 scanner, as well as datasets from a custom-made, low-cost mobile mapping system (MMS) named Simultaneous Localization And Mapping Multi-sensor roBOT (SLAMM-BOT) across diverse scenes, in which both outperformed their baseline solutions. SuperGlue performed best in high-feature scenes, whereas LoFTR performed best in low-feature or sparse data scenes. The LiDAR intensity layer had the strongest matches, but combining feature layers improved matching and reduced errors. Full article

Background/Objectives: The integration of digital imaging technologies in dentistry has revolutionized diagnostic and treatment practices, with panoramic radiographs playing a crucial role in detecting impacted teeth. Manual interpretation of these images is time consuming and error prone, highlighting the need for automated, accurate solutions. This study proposes an artificial intelligence (AI)-based model for detecting impacted teeth in panoramic radiographs, aiming to enhance accuracy and reliability. Methods: The proposed model combines YOLO (You Only Look Once) and RT-DETR (Real-Time Detection Transformer) models to leverage their strengths in real-time object detection and learning long-range dependencies, respectively. The integration is further optimized with the Weighted Boxes Fusion (WBF) algorithm, where WBF parameters are tuned using Bayesian optimization. A dataset of 407 labeled panoramic radiographs was used to evaluate the model’s performance. Results: The model achieved a mean average precision (mAP) of 98.3% and an F1 score of 96%, significantly outperforming individual models and other combinations. The results were expressed through key performance metrics, such as mAP and F1 scores, which highlight the model’s balance between precision and recall. Visual and numerical analyses demonstrated superior performance, with enhanced sensitivity and minimized false positive rates. Conclusions: This study presents a scalable and reliable AI-based solution for detecting impacted teeth in panoramic radiographs, offering substantial improvements in diagnostic accuracy and efficiency. The proposed model has potential for widespread application in clinical dentistry, reducing manual workload and error rates. Future research will focus on expanding the dataset and further refining the model’s generalizability. Full article

Earth observation (EO) satellites offer significant potential in wildfire detection and assessment due to their ability to provide fine spatial, temporal, and spectral resolutions. Over the past decade, satellite data have been systematically utilized to monitor wildfire dynamics and evaluate their impacts, leading to substantial advancements in wildfire management strategies. The present study contributes to this field by enhancing the frequency and accuracy of wildfire detection through advanced techniques for detecting, classifying, and segmenting wildfires using satellite imagery. Publicly available multi-sensor satellite data, such as Landsat, Sentinel-1, and Sentinel-2, from 2018 to 2020 were employed, providing temporal observation frequencies of up to five days, which represents a 25% increase compared to traditional monitoring approaches. Sophisticated algorithms were developed and implemented to improve the accuracy of fire detection while minimizing false alarms. The study evaluated the performance of three distinct models: an autoencoder, a U-Net, and a convolutional neural network (CNN), comparing their effectiveness in predicting wildfire occurrences. The results indicated that the CNN model demonstrated superior performance, achieving a fire detection accuracy of 82%, which is approximately 10% higher than the best-performing model in similar studies. This accuracy, coupled with the model’s ability to balance various performance metrics and learnable weights, positions it as a promising tool for real-time wildfire detection. The findings underscore the significant potential of optimized machine learning approaches in predicting extreme events, such as wildfires, and improving fire management strategies. Achieving 82% detection accuracy in real-world applications could drastically reduce response times, minimize the damage caused by wildfires, and enhance resource allocation for firefighting efforts, emphasizing the importance of continued research in this domain. Full article

Riverine environmental information includes important data to collect, and the data collection still requires personnel’s field surveys. These on-site tasks still face significant limitations (i.e., hard or danger to entry). In recent years, as one of the efficient approaches for data collection, air-vehicle-based Light Detection and Ranging technologies have already been applied in global environmental research, i.e., land cover classification (LCC) or environmental monitoring. For this study, the authors specifically focused on seven types of LCC (i.e., bamboo, tree, grass, bare ground, water, road, and clutter) that can be parameterized for flood simulation. A validated airborne LiDAR bathymetry system (ALB) and a UAV-borne green LiDAR System (GLS) were applied in this study for cross-platform analysis of LCC. Furthermore, LiDAR data were visualized using high-contrast color scales to improve the accuracy of land cover classification methods through image fusion techniques. If high-resolution aerial imagery is available, then it must be downscaled to match the resolution of low-resolution point clouds. Cross-platform data interchangeability was assessed by comparing the interchangeability, which measures the absolute difference in overall accuracy (OA) or macro-F1 by comparing the cross-platform interchangeability. It is noteworthy that relying solely on aerial photographs is inadequate for achieving precise labeling, particularly under limited sunlight conditions that can lead to misclassification. In such cases, LiDAR plays a crucial role in facilitating target recognition. All the approaches (i.e., low-resolution digital imagery, LiDAR-derived imagery and image fusion) present results of over 0.65 OA and of around 0.6 macro-F1. The authors found that the vegetation (bamboo, tree, grass) and road species have comparatively better performance compared with clutter and bare ground species. Given the stated conditions, differences in the species derived from different years (ALB from year 2017 and GLS from year 2020) are the main reason. Because the identification of clutter species includes all the items except for the relative species in this research, RGB-based features of the clutter species cannot be substituted easily because of the 3-year gap compared with other species. Derived from on-site reconstruction, the bare ground species also has a further color change between ALB and GLS that leads to decreased interchangeability. In the case of individual species, without considering seasons and platforms, image fusion can classify bamboo and trees with higher F1 scores compared to low-resolution digital imagery and LiDAR-derived imagery, which has especially proved the cross-platform interchangeability in the high vegetation types. In recent years, high-resolution photography (UAV), high-precision LiDAR measurement (ALB, GLS), and satellite imagery have been used. LiDAR measurement equipment is expensive, and measurement opportunities are limited. Based on this, it would be desirable if ALB and GLS could be continuously classified by Artificial Intelligence, and in this study, the authors investigated such data interchangeability. A unique and crucial aspect of this study is exploring the interchangeability of land cover classification models across different LiDAR platforms. Full article

The resting postures of piglets are crucial indicators for assessing their health status and environmental comfort. This study proposes a resting posture recognition method for piglets during lactation based on the PPR-YOLO model, aiming to enhance the detection accuracy and classification capability for different piglet resting postures. Firstly, to address the issue of numerous sows and piglets in the farrowing house that easily occlude each other, an image edge detection algorithm is employed to precisely locate the sow’s farrowing bed area. By cropping the images, irrelevant background interference is reduced, thereby enhancing the model’s recognition accuracy. Secondly, to overcome the limitations of the YOLOv11 model in fine feature extraction and small object detection, improvements are made, resulting in the proposed PPR-YOLO model. Specific enhancements include the introduction of a multi-branch Conv2 module to enrich feature extraction capabilities and the adoption of an inverted bottleneck IBCNeck module, which expands the number of channels and incorporates a channel attention mechanism. This strengthens the model’s ability to capture and differentiate subtle posture features. Additionally, in the post-processing stage, the relative positions between sows and piglets are utilized to filter out piglets located outside the sow region, eliminating interference from sow nursing behaviors in resting posture recognition, thereby ensuring the accuracy of posture classification. The experimental results show that the proposed method achieves accurate piglet posture recognition, outperforming mainstream object detection algorithms. Ablation experiments validate the effectiveness of image cropping and model enhancements in improving performance. This method provides effective technical support for the automated monitoring of piglet welfare in commercial farms and holds promising application prospects. Full article

In response to the rising frequency of traffic accidents and growing concerns regarding driving safety, the identification and analysis of dangerous driving behaviors have emerged as critical components in enhancing road safety. In this paper, the research progress in the recognition methods of dangerous driving behavior based on deep learning is analyzed. Firstly, the data collection methods are categorized into four types, evaluating their respective advantages, disadvantages, and applicability. While questionnaire surveys provide limited information, they are straightforward to conduct. The vehicle operation data acquisition method, being a non-contact detection, does not interfere with the driver’s activities but is susceptible to environmental factors and individual driving habits, potentially leading to inaccuracies. The recognition method based on dangerous driving behavior can be monitored in real time, though its effectiveness is constrained by lighting conditions. The precision of physiological detection depends on the quality of the equipment. Then, the collected big data are utilized to extract the features related to dangerous driving behavior. The paper mainly classifies the deep learning models employed for dangerous driving behavior recognition into three categories: Deep Belief Network (DBN), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN). DBN exhibits high flexibility but suffers from relatively slow processing speeds. CNN demonstrates excellent performance in image recognition, yet it may lead to information loss. RNN possesses the capability to process sequential data effectively; however, training these networks is challenging. Finally, this paper concludes with a comprehensive analysis of the application of deep learning-based dangerous driving behavior recognition methods, along with an in-depth exploration of their future development trends. As computer technology continues to advance, deep learning is progressively replacing fuzzy logic and traditional machine learning approaches as the primary tool for identifying dangerous driving behaviors. Full article

Obstructive Sleep Apnea (OSA) is a prevalent chronic sleep-related breathing disorder characterized by partial or complete airway obstruction. The expensive, time-consuming, and labor-intensive nature of the gold-standard approach, polysomnography (PSG), and the lack of regular monitoring of patients’ daily lives with existing solutions motivates the development of clinical support for enhanced prognosis. In this study, we utilize image representations of sleep stages and contextual patient-specific data, including medical history and stage durations, to investigate the use of wearable devices for OSA screening and comorbid conditions. For this purpose, we leverage the publicly available Wisconsin Sleep Cohort (WSC) dataset. Given that wearable devices are adept at detecting sleep stages (often using proprietary algorithms), and medical history data can be efficiently captured through simple binary (yes/no) responses, we seek to explore neural network models with this. Without needing access to the raw physiological signals and using epoch-wise sleep scores and demographic data, we attempt to validate the effectiveness of screening capabilities and assess the interplay between sleep stages, OSA, insomnia, and depression. Our findings reveal that sleep stage representations combined with demographic data enhance the precision of OSA screening, achieving F1 scores of up to 69.40. This approach holds potential for broader applications in population health management as a plausible alternative to traditional diagnostic approaches. However, we find that purely modality-agnostic sleep stages for a single night and routine lifestyle information by themselves may be insufficient for clinical utility, and further work accommodating individual variability and longitudinal data is needed for real-world applicability. Full article

This study proposes a method for estimating the spectral images of fluorescence spectral distributions emitted from plant grains and leaves without using a spectrometer. We construct two types of multiband imaging systems with six channels, using ordinary off-the-shelf cameras and a UV light. A mobile phone camera is used to detect the fluorescence emission in the blue wavelength region of rice grains. For plant leaves, a small monochrome camera is used with additional optical filters to detect chlorophyll fluorescence in the red-to-far-red wavelength region. A ridge regression approach is used to obtain a reliable estimate of the spectral distribution of the fluorescence emission at each pixel point from the acquired image data. The spectral distributions can be estimated by optimally selecting the ridge parameter without statistically analyzing the fluorescence spectra. An algorithm for optimal parameter selection is developed using a cross-validation technique. In experiments using real rice grains and green leaves, the estimated fluorescence emission spectral distributions by the proposed method are compared to the direct measurements obtained with a spectroradiometer and the estimates obtained using the minimum norm estimation method. The estimated images of fluorescence emissions are presented for rice grains and green leaves. The reliability of the proposed estimation method is demonstrated. Full article

In recent years, convolutional neural network (CNN)-based and transformer-based approaches have made strides in improving the performance of hyperspectral image (HSI) classification tasks. However, misclassifications are unavoidable in the aforementioned methods, with a considerable number of these issues stemming from the overlapping embedding spaces among different classes. This overlap results in samples being allocated to adjacent categories, thus leading to inaccurate classifications. To mitigate these misclassification issues, we propose a novel discrete vector representation (DVR) strategy for enhancing the performance of HSI classifiers. DVR establishes a discrete vector quantification mechanism to capture and store distinct category representations in the codebook between the encoder and classification head. Specifically, DVR comprises three components: the Adaptive Module (AM), Discrete Vector Constraints Module (DVCM), and auxiliary classifier (AC). The AM aligns features derived from the backbone to the embedding space of the codebook. The DVCM employs category representations from the codebook to constrain encoded features for a rational feature distribution of distinct categories. To further enhance accuracy, the AC correlates discrete vectors with category information obtained from labels by penalizing these vectors and propagating gradients to the encoder. It is worth noting that DVR can be seamlessly integrated into HSI classifiers with diverse architectures to enhance their performance. Numerous experiments on four HSI benchmarks demonstrate that our DVR scheme improves the classifiers’ performance in terms of both quantitative metrics and visual quality of classification maps. We believe DVR can be applied to more models in the future to enhance their performance and provide inspiration for tasks such as sea ice detection and algal bloom prediction in the marine domain. Full article

The increasing demand for higher resolution and faster machinery in silicon wafer inspection is driven by the rise in electronic device production and the decreasing size of microchips. This paper presents the design and implementation of a device capable of accurately measuring the surface of silicon wafers using the stitching technique. We propose an optical system design for measuring the surface profile, specifically targeting the roughness and nanotopography of a silicon wafer. The device achieves a lateral resolution of 7.56 μm and an axial resolution of 1 nm. It can measure a full 300-mm wafer in approximately 60 min, acquiring around 400 million data points. The technique utilized is a wavefront phase sensor, which reconstructs the surface shape using two images displaced a certain distance from the conjugate plane in the image space of a 4f system. The study details the calibration process and provides a method for converting local measurement coordinates to global coordinates. Quantitative phase imaging was obtained by using the wave front intensity image algorithm. The conclusive results validate the method different metrics over a wafer with bonded dies. In addition, the device demonstrates the ability to distinguish different dies that are thinned along with die-to-wafer bonding onto a carrier wafer to obtain the difference in coplanarity between the die and its surroundings as well as to detect defects during the die-to-wafer bonding. Lastly, the residual stress in the thin film deposited over the die is obtained using the Stoney model. Full article

In the context of escalating network adversarial challenges, effectively identifying a Webshell processed using evasion techniques such as encoding, obfuscation, and nesting remains a critical challenge in the field of cybersecurity. To address the poor detection performance of the existing Webshell detection methods for evasion samples, this study proposes a multimodal feature fusion-based evasion Webshell detection method (MMF-Detect). This method extracts RGB image features and textual vector features from two modalities: the visual and semantic modalities of Webshell file content. A multimodal feature fusion classification model was designed to classify features from both modalities to achieve Webshell detection. The multimodal feature fusion classification model consists of a text classifier based on a large language model (CodeBERT), an image classifier based on a convolutional neural network (CNN), and a decision-level feature fusion mechanism. The experimental results show that the MMF-Detect method not only demonstrated excellent performance in detecting a conventional Webshell but also achieved an accuracy of 99.47% in detecting an evasive Webshell, representing a significant improvement compared to traditional models. Full article

Avian eggs are products of consumer demand, with modern methodologies for their morphometric analysis used for improving quality, productivity and marketability. Such studies open up numerous prospects for the introduction of artificial intelligence (AI) and deep learning (DL). We first consider the state of the art of DL in the poultry industry, e.g., image recognition and applications for the detection of egg cracks, egg content and freshness. We comment on how algorithms need to be properly trained and ask what information can be gleaned from egg shape. Considering the geometry of egg profiles, we revisit the Preston–Biggins egg model, the Hügelschäffer’s model, universal egg models, principles of egg universalism and “The Main Axiom”, proposing a series of postulates to evaluate the legitimacy and practical application of various mathematical models. We stress that different models have pros and cons, and using them in combination may yield more useful results than individual use. We consider the classic egg shape index alongside other alternatives, drawing conclusions about the importance of indices in the context of applying DL going forward. Examining egg weight, volume, surface area and air cell calculations, we consider how DL might be applied, e.g., for egg storage. The value of DL in egg studies is in pre-incubation egg sorting, the optimization of storage periods and incubation regimes, and the index representation of dimensional characteristics. Each index can thus be combined to provide a synergy that is on the threshold of many scientific discoveries, technological achievements and industrial successes facilitated through AI and DL. Full article