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Review of Modern Forest Fire Detection Techniques: Innovations in image processing and deep learning

INFORMATION 2024年第9期15卷 538页

作者： Ozel, Berk Alam, Muhammad Shahab Khan, Muhammad Umer Atilim Univ Dept Mechatron Engn TR-06830 Ankara Turkiye Gebze Tech Univ Def Technol Inst TR-41400 Gebze Turkiye

Fire detection and extinguishing systems are critical for safeguarding lives and minimizing property damage. These systems are especially vital in combating forest fires. In recent years, several forest fires have set records for their size, duration, and level of destruction. Traditional fire detection methods, such as smoke and heat sensors, have limitations, prompting the development of innovative approaches using advanced technologies. Utilizing image processing, computer vision, and deep learning algorithms, we can now detect fires with exceptional accuracy and respond promptly to mitigate their impact. In this article, we conduct a comprehensive review of articles from 2013 to 2023, exploring how these technologies are applied in fire detection and extinguishing. We delve into modern techniques enabling real-time analysis of the visual data captured by cameras or satellites, facilitating the detection of smoke, flames, and other fire-related cues. Furthermore, we explore the utilization of deep learning and machine learning in training intelligent algorithms to recognize fire patterns and features. Through a comprehensive examination of current research and development, this review aims to provide insights into the potential and future directions of fire detection and extinguishing using image processing, computer vision, and deep learning.

关键词： artificial intelligence deep learning detection fire flame forest fire smoke wildfire

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deep learning with image processing for Object Detection in Traffic Cameras

Deep Learning with Image Processing for Object Detection in ...

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International Conference on Trends in Electronics and Informatics (ICEI)

作者： Santhosh D S. Prabu Department of Computer Applications Hindustan Institution of Technology and Science Chennai Tamil Nadu

ISBN: (数字)9798331544607

ISBN: (纸本)9798331544614

Traffic surveillance is a key factor in ITS whereby accurate and real-time object detection assures improvement of road safety and traffic management. This paper advances a deep-learning-based perspective that combines image processing techniques with convolutional neural networks (CNNs) to maximize the object detection accuracy over the traffic camera feed. The filtering, edge detection, and feature extraction of images for data preprocessing enhance the model performance. This setup provides high inference speed with reliable detection of vehicles via YOLO (You Only Look Once) and Faster R-CNN. The obtained experimental results from our evaluation show great enhancement in the detection accuracy which places the model in a position for real-time application in traffic surveillance. The study also investigates the computational efficiency and real-life boundaries for implementing this strategy, providing an exhaustive account of the suitability of the proposed method in larger-scale applications in ITS.

关键词： YOLO deep learning Computer vision Accuracy Surveillance Computational modeling Traffic control Cameras real-time systems Convolutional neural networks

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Enhanced image processing for Automobile Accident Detection Using deep learning 3

Enhanced Image Processing for Automobile Accident Detection ...

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3rd International Conference on Smart Technologies and Systems for Next Generation Computing, ICSTSN 2024

作者： Usharani, S. Dhanushkumar, J. Manju Bala, P. Balachandar, A. IFET College of Engineering An Autonomous Institution Department of Artificial Intelligence and Machine Learning Villupuram India IFET College of Engineering An Autonomous Institution Department of Computer Science and Engineering Villupuram India

ISBN: (纸本)9798350391565

Road safety can be creatively increased by utilizing systems for reporting and detecting accidents use the YOLO algorithm. Yolo, which stands for "You Only Look Once,"is a sophisticated object recognition system that can identify and pinpoint objects in live video streams. By identifying accidents and notifying emergency services, the system lowers the reaction times and increases the possibility of saving lives by utilizing the YOLO algorithm. The object detection and alarm systems are the two primary parts of the proposed system. The YOLO method is employed by the object recognition module to look for accidents in live video broadcasts. A module of accident photos was used to teach the system to accurately identify incidents. When an accident is discovered, an alert system is activated. The location of the accident and a brief account of what transpired are communicated to the emergency services by the alert system. This information is communicated to emergency services through a wireless communication network, which expedites response times and increases the likelihood of saving lives. Positive results were obtained by testing the system using the images and an accident module. It was shown that the warning system could react in a couple of seconds and that the YOLO algorithm could identify accidents having a precision of around 94. Highways, busy intersections, and other high-risk areas may have systems in place to increase traffic safety and lower the number of accidents. Developing crash detection and warning systems using deep learning using the YOLO technique is one way to potentially increase road safety. With the use of technology, problems may be precisely identified in real-time video feeds, alerting emergency services and potentially increasing survival rates and reaction times. © 2024 IEEE.

关键词： image enhancement

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Automatic Volume Control Using image processing and deep learning Techniques: A Review

Automatic Volume Control Using Image Processing and Deep Lea...

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Intelligent Systems for Cybersecurity (ISCS), International Conference on

作者： Kinshuk Theraja Sakshi Gupta deepak Kumar Gupta Amity Institute of Information and Technology Amity University Noida

ISBN: (数字)9798350375237

ISBN: (纸本)9798350375244

This paper presents an innovative approach to automatic volume control using image processing and deep learning techniques. The ability to automatically adjust volume levels based on environmental factors and user preferences has significant implications for various audio applications, including teleconferencing systems, smart devices, and public address systems. By combining image processing algorithms with deep learning models, this paper aims to develop a robust and adaptive volume control system capable of accurately adjusting audio levels in real-time. The paper discusses the theoretical foundations, technical implementation, experimental results, and potential applications of the proposed automatic volume control system.

关键词： deep learning Visualization Teleconferencing Solid modeling image processing Software algorithms Process control

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deep learning Methods of Satellite image processing for Monitoring of Flood Dynamics in the Ganges Delta, Bangladesh

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WATER 2024年第8期16卷 1141页

作者： Lemenkova, Polina Univ Salzburg Fac Digital & Analyt Sci Dept Geoinformat Schillerstr 30 A-5020 Salzburg Austria

Mapping spatial data is essential for the monitoring of flooded areas, prognosis of hazards and prevention of flood risks. The Ganges River Delta, Bangladesh, is the world's largest river delta and is prone to floods that impact social-natural systems through losses of lives and damage to infrastructure and landscapes. Millions of people living in this region are vulnerable to repetitive floods due to exposure, high susceptibility and low resilience. Cumulative effects of the monsoon climate, repetitive rainfall, tropical cyclones and the hydrogeologic setting of the Ganges River Delta increase probability of floods. While engineering methods of flood mitigation include practical solutions (technical construction of dams, bridges and hydraulic drains), regulation of traffic and land planning support systems, geoinformation methods rely on the modelling of remote sensing (RS) data to evaluate the dynamics of flood hazards. Geoinformation is indispensable for mapping catchments of flooded areas and visualization of affected regions in real-time flood monitoring, in addition to implementing and developing emergency plans and vulnerability assessment through warning systems supported by RS data. In this regard, this study used RS data to monitor the southern segment of the Ganges River Delta. Multispectral Landsat 8-9 OLI/TIRS satellite images were evaluated in flood (March) and post-flood (November) periods for analysis of flood extent and landscape changes. deep learning (DL) algorithms of GRASS GIS and modules of qualitative and quantitative analysis were used as advanced methods of satellite image processing. The results constitute a series of maps based on the classified images for the monitoring of floods in the Ganges River Delta.

关键词： machine learning deep learning flood monsoon cartography geoinformatics remote sensing satellite image geospatial analysis GRASS GIS

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Research on rail surface crack identification and detection based on deep learning and image processing 2

Research on rail surface crack identification and detection ...

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2nd International Conference on 3D Immersion, Interaction and Multi-Sensory Experiences, ICDIIME 2023

作者： Yu, Wang Rong, Miaobing Min, Daihu Ying, Zhang Guang, Zhaoyi Southwest Jiaotong University State Key Laboratory of Traction Power Chengdu Sichuan610031 China College of Advanced Manufacturing Technology Guangdong Institute of Mechanical and Electrical Technology Guangzhou510550 China

ISBN: (纸本)9798350323818

With the implementation of the development program of our country's transportation country, the traffic construction has welcomed a booming development, the passenger transportation and freight transportation increasing year by year, but it brings more and more high requirements on the quality of the steel rail, the steel rail due to the long-term service and all kinds of defects appear, especially with the rolling contact fatigue cracks. A method and system of rail surface crack identification and detection based on deep learning is proposed. The crack features can be accurately extracted from the complex image of rail surface for real-time crack identification and detection. The crack width can be identified as 0.1mm, and the recognition speed can reach 1-2ms. The rapid identification and detection of rail surface cracks are completed. © 2023 IEEE.

关键词： Freight transportation

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Dental Periapical Lesion Detection with Retinex image processing and an Efficient deep learning Model

Dental Periapical Lesion Detection with Retinex Image Proces...

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International Conference on Trends in Electronics and Informatics (ICEI)

作者： C.A. Aslin Christo Julia Punitha Malar Dhas Deva Priya Computer Science and Engineering Karunya University Coimbatore India

ISBN: (数字)9798331544607

ISBN: (纸本)9798331544614

Dental periapical lesions help doctors understand your oral health needs and must be found accurately to deliver proper treatment. The research introduces a new way to find dental periapical lesions by combining Retinex image processing with YOLOv8 object detection. The Retinex method lets the model detect periapical lesions more accurately because it enhances image details while removing picture noise. Test outcomes show enhanced lesion detection that performs well with precise accuracy in tough image scenarios. The research demonstrates how advanced image processing helps medical imaging deep learning systems work better. The Objective of this work is to improve visibility of periapical lesions by Retinex based enhancement and fast detection through YOLOv8.

关键词： deep learning Radiography Visualization Object detection real-time systems Skin Dentistry Lesions X-ray imaging Biomedical imaging

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Underwater image processing and Target Detection Algorithm Based on deep learning

Underwater Image Processing and Target Detection Algorithm B...

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作者： Zheng, Yangning Yang, Yang School of Medicine Nanchang Institute of Technology Jiangxi Nanchang China School of Electronics and Information Nanchang Institute of Technology Jiangxi Nanchang China

With the growth of the earth’s population, people’s demand for space and resources continues to grow, making marine environmental research and exploration a new field that human development needs. The composition of the marine environment is unique to humans, and the application of intelligent underwater vehicle equipment with the ability to autonomously detect and recognize the environment can expand the scope of human research in the ocean. This article focuses on the research of underwater image processing and target detection algorithms based onDL, and understands the relevant theories of underwater image processing and target detection based on the literature, and then the underwater image processing and target detection algorithms based on DL In the design, in order to improve the accuracy of target detection, the DL model is optimized, and the optimized model is tested. The test result shows that if the Atrous convolution operation is added, the calculation amount of the network model will increase, resulting in *** speed drops to 50FPS. But after adding Atrous processing, the detection accuracy will be improved accordingly. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： deep learning

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License Plate reader with PUC Details using image processing and deep learning 2

License Plate reader with PUC Details using Image Processing...

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2nd International Conference on Smart Technologies and Systems for Next Generation Computing, ICSTSN 2023

作者： Patil, R.S. Deshpande, Sakshi Khan, Hassan Mhatre, Prajakta Bharati Vidyapeeth College of Engineering Department of Electronics and Telecommunication Engineering Maharashtra Mumbai India

ISBN: (纸本)9798350348002

As each vehicle is uniquely acknowledged by its license plate, the Transport System places a high priority on finding and recognizing of license plates. The news is constantly reporting on accidents and missing cars. Authorities must acknowledge all of these unlawful acts. As a result, research into the identification and recognition of vehicle number plates is ongoing. However, identifying a vehicle's number plate has always been difficult for a number of reasons, such as brightness changes, shadows cast by moving vehicles, erratic license plate character types, different plate styles, and color effects caused by the surroundings. In this system, Number plate of vehicle is detected from a live video or an image. There is image preprocessing and segmentation done on the live video or number plate image. deep learning model methods are used, the characters from it are separated and then each character gets recognized. This helps to collect the vehicle overall project then the capabilities of different techniques into one integrated automatic system are summarized. This kind of systems can be implemented on the roadside and makes a real time comparison between passing car and list of stolen cars. This detected license plate number could also be used in car parking systems. PUC which stands for Pollution Under Control, where emission levels of vehicles and the regular renovation of the PUC certificate is done or not is verified and the details are shown. This will help in keeping an overall check on vehicles and the task which most of the places do manually to check the PUC certificate for checking status, can be verified quickly and the fine can be implemented as per so. © 2023 IEEE.

关键词： Optical character recognition

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Experimental Analysis of Malarial Parasites Infection by using deep learning based on Medical image processing Logic

Experimental Analysis of Malarial Parasites Infection by usi...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： N Meenakshisundaram Sajiv G Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Science (SIMATS) Saveetha University Chennai India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Malaria remains a significant global health challenge, particularly in resource-limited regions, necessitating accurate and rapid diagnostic tools. This study introduces deepMalariaNet, a deep learning model developed for detecting and classifying malarial parasites using the “Malaria Parasite image - Malaria Species” dataset from Kaggle. The model employs Residual Attention Mechanisms and Parallel Convolutional Stacks (PCS) to improve diagnostic accuracy by focusing on critical image regions and capturing multi-scale features. Experimental results demonstrate that deepMalariaNet achieves 98.5% accuracy for binary classification (infected vs. non-infected) and 95.2% for multiclass classification (species identification). The model's robustness is validated through 10-fold cross-validation and ablation studies, and it outperforms state-of-the-art models such as ResNet-50 and DenseNet-121 in both accuracy and inference time. deepMalariaNet shows significant promise for real-time malaria detection in clinical settings, contributing to early and accurate diagnosis, which is crucial for effective malaria control and treatment.

关键词： deep learning Accuracy Attention mechanisms Malaria Parasitic diseases Sensitivity and specificity Metadata Robustness real-time systems Integrated circuit modeling

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