MBGPIN: Multi-Branch Generative Prior Integration Network for Super-Resolution Satellite Imagery

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(5), P. 805 - 805

Published: Feb. 25, 2025

Achieving super-resolution with satellite images is a critical task for enhancing the utility of remote sensing data across various applications, including urban planning, disaster management, and environmental monitoring. Traditional interpolation methods often fail to recover fine details, while deep-learning-based approaches, convolutional neural networks (CNNs) generative adversarial (GANs), have significantly advanced performance. Recent studies explored large-scale models, such as Transformer-based architectures diffusion demonstrating improved texture realism generalization diverse datasets. However, these frequently high computational costs require extensive datasets training, making real-world deployment challenging. We propose multi-branch prior integration network (MBGPIN) address limitations. This novel framework integrates multiscale feature extraction, hybrid attention mechanisms, priors derived from pretrained VQGAN models. The dual-pathway architecture MBGPIN includes extraction pathway spatial features external guidance, dynamically fused using an adaptive fusion (AGPF) module. Extensive experiments on benchmark UC Merced, NWPU-RESISC45, RSSCN7 demonstrate that achieves superior performance compared state-of-the-art methods, delivers higher peak signal-to-noise ratio (PSNR) structural similarity index measure (SSIM) scores preserving high-frequency details complex textures. model also significant efficiency, reduced floating point operations (FLOPs) faster inference times, it scalable applications.

Language: Английский

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Innovative Driver Monitoring Systems and On-Board-Vehicle Devices in a Smart-Road Scenario Based on the Internet of Vehicle Paradigm: A Literature and Commercial Solutions Overview

Sensors, Journal Year: 2025, Volume and Issue: 25(2), P. 562 - 562

Published: Jan. 19, 2025

In recent years, the growing number of vehicles on road have exacerbated issues related to safety and traffic congestion. However, advent Internet Vehicles (IoV) holds potential transform mobility, enhance management safety, create smarter, more interconnected networks. This paper addresses key concerns, focusing driver condition detection, vehicle monitoring, management. Specifically, various models proposed in literature for monitoring driver’s health detecting anomalies, drowsiness, impairment due alcohol consumption are illustrated. The describes architectures, including diagnostic solutions identifying malfunctions, instability while driving slippery or wet roads. It also covers systems classifying style, as well tire emissions monitoring. Moreover, provides a detailed overview solutions, along with environmental conditions, sensors used Machine Learning (ML) algorithms implemented. Finally, this review presents an innovative commercial illustrating advanced devices assessment,

Language: Английский

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Fire Detection and Notification Method in Ship Areas Using Deep Learning and Computer Vision Approaches

Sensors, Journal Year: 2023, Volume and Issue: 23(16), P. 7078 - 7078

Published: Aug. 10, 2023

Fire incidents occurring onboard ships cause significant consequences that result in substantial effects. Fires on can have extensive and severe wide-ranging impacts matters such as the safety of crew, cargo, environment, finances, reputation, etc. Therefore, timely detection fires is essential for quick responses powerful mitigation. The study this research paper presents a fire technique based YOLOv7 (You Only Look Once version 7), incorporating improved deep learning algorithms. architecture, with an E-ELAN (extended efficient layer aggregation network) its backbone, serves basis our system. Its enhanced feature fusion makes it superior to all predecessors. To train model, we collected 4622 images various ship scenarios performed data augmentation techniques rotation, horizontal vertical flips, scaling. Our through rigorous evaluation, showcases capabilities recognition improve maritime safety. proposed strategy successfully achieves accuracy 93% detecting minimize catastrophic incidents. Objects having visual similarities may lead false prediction by but be controlled expanding dataset. However, model utilized real-time detector challenging environments small-object detection. Advancements models hold potential enhance measures, exhibits potential. Experimental results proved method used protection monitoring port areas. Finally, compared performance those recently reported fire-detection approaches employing widely matrices test classification achieved.

Language: Английский

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When, Where, and Which?: Navigating the Intersection of Computer Vision and Generative AI for Strategic Business Integration

IEEE Access, Journal Year: 2023, Volume and Issue: 11, P. 127202 - 127215

Published: Jan. 1, 2023

In today's rapidly evolving digital landscape, Artificial Intelligence (AI) exerts a profound influence on our daily lives, from predictive text in emails to the ever-present virtual assistants like Alexa and Siri. This scholarly article embarks comprehensive exploration of expansive world Intelligence, with keen focus domains generative AI computer vision. Our objective is provide businesses nuanced in-depth understanding these critical subfields. By doing so, we empower organizations make informed strategic decisions regarding adoption vision technologies. ultimate goal equip knowledge insights necessary harness potential effectively, driving innovation bolstering their competitive edge an increasingly technology-driven world.

Language: Английский

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Fire and Smoke Detection in Complex Environments

Fire, Journal Year: 2024, Volume and Issue: 7(11), P. 389 - 389

Published: Oct. 29, 2024

Fire detection is a critical task in environmental monitoring and disaster prevention, with traditional methods often limited their ability to detect fire smoke real time over large areas. The rapid identification of both indoor outdoor environments essential for minimizing damage ensuring timely intervention. In this paper, we propose novel approach by integrating vision transformer (ViT) the YOLOv5s object model. Our modified model leverages attention-based feature extraction capabilities ViTs improve accuracy, particularly complex where fires may be occluded or distributed across regions. By replacing CSPDarknet53 backbone ViT, able capture local global dependencies images, resulting more accurate under challenging conditions. We evaluate performance proposed using comprehensive Smoke Detection Dataset, which includes diverse real-world scenarios. results demonstrate that our outperforms baseline YOLOv5 variants terms precision, recall, mean average precision (mAP), achieving [email protected] 0.664 recall 0.657. ViT shows significant improvements detecting smoke, scenes backgrounds varying scales. findings suggest integration as offers promising real-time urban natural environments.

Language: Английский

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Processing and Integration of Multimodal Image Data Supporting the Detection of Behaviors Related to Reduced Concentration Level of Motor Vehicle Users

Electronics, Journal Year: 2024, Volume and Issue: 13(13), P. 2457 - 2457

Published: June 23, 2024

This paper introduces a comprehensive framework for the detection of behaviors indicative reduced concentration levels among motor vehicle operators, leveraging multimodal image data. By integrating dedicated deep learning models, our approach systematically analyzes RGB images, depth maps, and thermal imagery to identify driver drowsiness distraction signs. Our novel contribution includes utilizing state-of-the-art convolutional neural networks (CNNs) bidirectional long short-term memory (Bi-LSTM) effective feature extraction classification across diverse scenarios. Additionally, we explore various data fusion techniques, demonstrating their impact on improving accuracy. The significance this work lies in its potential enhance road safety by providing more reliable efficient tools real-time monitoring attentiveness, thereby reducing risk accidents caused fatigue. proposed methods are thoroughly evaluated using benchmark dataset, with results showing substantial capabilities leading development safety-enhancing technologies vehicular environments. primary challenge addressed study is states not relying lighting conditions. solution employs integration, encompassing RGB, thermal, ensure robust accurate regardless external variations

Language: Английский

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Explainable Lightweight Block Attention Module Framework for Network-Based IoT Attack Detection

Future Internet, Journal Year: 2023, Volume and Issue: 15(9), P. 297 - 297

Published: Sept. 1, 2023

In the rapidly evolving landscape of internet usage, ensuring robust cybersecurity measures has become a paramount concern across diverse fields. Among numerous cyber threats, denial service (DoS) and distributed (DDoS) attacks pose significant risks, as they can render websites servers inaccessible to their intended users. Conventional intrusion detection methods encounter substantial challenges in effectively identifying mitigating these due widespread nature, intricate patterns, computational complexities. However, by harnessing power deep learning-based techniques, our proposed dense channel-spatial attention model exhibits exceptional accuracy detecting classifying DoS DDoS attacks. The successful implementation framework addresses posed imbalanced data its potential for real-world applications. By leveraging mechanism, precisely identify classify attacks, bolstering defenses servers. high rates achieved different datasets reinforce robustness approach, underscoring efficacy enhancing capabilities. As result, holds promise scenarios, contributing ongoing efforts safeguard against threats an increasingly interconnected digital landscape. Comparative analysis with current reveals superior performance model. We 99.38%, 99.26%, 99.43% Bot-IoT, CICIDS2017, UNSW_NB15 datasets, respectively. These remarkable results demonstrate capability approach accurately detect various types assaults. inherent strengths learning, such pattern recognition feature extraction, overcomes limitations traditional methods, efficiency systems.

Language: Английский

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Fall Detection System Based on Point Cloud Enhancement Model for 24 GHz FMCW Radar

Sensors, Journal Year: 2024, Volume and Issue: 24(2), P. 648 - 648

Published: Jan. 19, 2024

Automatic fall detection plays a significant role in monitoring the health of senior citizens. In particular, millimeter-wave radar sensors are relevant for human pose recognition an indoor environment due to their advantages privacy protection, low hardware cost, and wide range working conditions. However, low-quality point clouds from 4D diminish reliability detection. To improve accuracy, conventional methods utilize more costly hardware. this study, we propose model that can provide high-quality three-dimensional cloud images body at cost. accuracy effectiveness detection, system extracts distribution features through small antenna arrays is developed. The proposed achieved 99.1% 98.9% on test datasets pertaining new subjects environments, respectively.

Language: Английский

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Association of Visual-Based Signals with Electroencephalography Patterns in Enhancing the Drowsiness Detection in Drivers with Obstructive Sleep Apnea

Sensors, Journal Year: 2024, Volume and Issue: 24(8), P. 2625 - 2625

Published: April 19, 2024

Individuals with obstructive sleep apnea (OSA) face increased accident risks due to excessive daytime sleepiness. PERCLOS, a recognized drowsiness detection method, encounters challenges from image quality, eyewear interference, and lighting variations, impacting its performance, requiring validation through physiological signals. We propose visual-based scoring using adaptive thresholding for eye aspect ratio OpenCV Dlib video recordings. This technique identified 453 (PERCLOS ≥ 0.3 || CLOSDUR 2 s) 474 wakefulness episodes < among fifty OSA drivers in 50 min driving simulation while wearing six-channel EEG electrodes. Applying discrete wavelet transform, we derived ten features, correlated them various criteria, assessed the sensitivity of brain regions individual channels. Among these theta–alpha-ratio exhibited robust mapping (94.7%) scoring, followed by delta–alpha-ratio (87.2%) delta–theta-ratio (86.7%). Frontal area (86.4%) channel F4 (75.4%) aligned most theta–alpha-ratio, frontal, occipital regions, particularly channels O2, displayed superior alignment across multiple features. Adding frontal or could correlate all patterns, reducing hardware needs. Our work potentially enhance real-time reliability assess fitness drive drivers.

Language: Английский

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Yawning Detection for Cognitive Distraction in Drivers Using AlexNet: A Deep Learning Approach

Procedia Computer Science, Journal Year: 2025, Volume and Issue: 252, P. 63 - 72

Published: Jan. 1, 2025

Language: Английский

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