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Trends in Material Science and Inventive Materials (ICTMIM), International Conference on

作者： U.v. Anbazhagu A. Alice Blessie R. Santhana Krishnan G. Yamini J. Relin Francis Raj P. Sundaravadivel Department of Computing Technologies School of Computing College of Engineering and Technology Faculty of Engineering and Technology SRM Institute of Science and Technology Chennai India Department of Electronics and Communication Engineering Government College of Engineering Tirunelveli India Department of Electronics and Communication Engineering SCAD College of Engineering and Technology Cheranmhadevi India Department of English Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Artificial Intelligence and Machine Learning Saveetha Engineering College Chennai India

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

Wildfires pose a significant threat to ecosystems, biodiversity, and human settlements, with climate change and deforestation exacerbating the frequency and intensity. Conventional forest fire detection methods, including satellite-based monitoring and ground surveillance, suffer from limitations such as delayed detection, high false alarm rates, and dependency on manual intervention. Existing sensor-based detection systems often lack integration with intelligent decision-making frameworks, leading to inefficient fire prevention strategies. The absence of real-time processing and predictive capabilities further reduces the effectiveness of current methodologies in mitigating wildfire risks. This work presents an IoT-based deep learning approach utilizing Convolutional Neural Networks (CNNs) for real-time forest fire detection and prevention. Data is collected from satellite images, drone feeds, and IoT sensors, including temperature, humidity, gas concentration, and infrared readings. Advanced data preprocessing techniques, including image augmentation, normalization, and feature extraction, enhance the robustness of the model. A CNN-based classification model is implemented to analyze fire patterns and assess risk levels. The system is trained using sensor data and image datasets, ensuring high detection accuracy with minimal false positives. The key features of this methodology include real-time wildfire detection, automated risk assessment, and deployment on IoT edge devices for immediate decision-making. The integration of sensor-driven insights with deep learning improves early detection capabilities while reducing reliance on manual surveillance. Efficient data handling and cloud-based alert mechanisms enable proactive measures against fire outbreaks. The proposed system enhances wildfire monitoring by ensuring fast response times, improved accuracy, and scalable deployment in fire-prone regions.

关键词： Deep learning Wildfires Accuracy Prevention and mitigation Surveillance Decision making Manuals Real-time systems Satellite images Time factors Climate change Fires Detection algorithms

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Developing Spherical Mobile Devices for Indoor Exploration

Developing Spherical Mobile Devices for Indoor Exploration

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Conference on systems of Signal Synchronization, Generating and processing in Telecommunications (SYNCHROINFO)

作者： Potapov, E., v Ipatov, A. A. Priorov, A. L. Romanov, A. A. PG Demidov Yaroslavl State Univ Yaroslavl Russia

ISBN: (纸本)9781728160726

Computer vision systems and algorithms are designed to process digital images and extract necessary information from it. In this research we propose computer vision system consisting of several spherical mobile devices with digital camera and microcomputer inside. Device's design, basic characteristics and current results, as well as steps for further actions to improve the system are described below in the paper. At the core of image processing OpenCv library and modern convolutional neural networks such as YOLOv3, MobileNETSSDv2 are used.

关键词： indoor exploration computer vision systems neural networks spherical robots

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image processing and pattern recognition-based car classification for intelligent transportation system

Image processing and pattern recognition-based car classific...

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6th Smart Cities Symposium (SCS 2022)

作者： v. Dange S. Pophale N. Raut F. D. Shadrach S. Chowdhury G. Ramya P. Dutta Vishwakarma Institute of technology Pune India KPR Institute of Engineering and Technology Coimbatore India Dept. of Electronics and Communication Engineering KPR Institute of Engineering and Technology Coimbatore India MCA Department Sri Venkateswara College of Engineering and Technology Chittoor India SITE School VIT Vellore Vellore India School of Engineering and Technology Amity University Kolkata Kolkata India

The combination of car model classification and accelerated advances in computer vision appears to have the potential to significantly revolutionize automated transportation systems. Pattern recognition and image processing-based car classification methods have been proposed in recent years to improve the performance of smart highway toll collection and monitoring of traffic systems. Unfortunately, all these algorithms are trained on a inadequate number of features derived from tiny datasets, which do not considered for real-time road traffic circumstances. To address these difficulties, proposed system presents a classification model for vehicle based on convolutional neural networks to enhance the resilience of car classifier model in real-time applications. To achieve real-time robustness in classification systems, this proposed system offers a Stanford car dataset of 16,185 images classified into 157 common vehicle classes. To begin, the car dataset was trained using Inception-v3, vGG16, and ResNet50 to evaluate their accuracy based on performance. As a result, a comparative study was carried out to assess the effectiveness of the proposed system.

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Fast Category-Hidden Adversarial Attack Against Semantic image Segmentation

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE systems 2021年第1期14卷 1823-1830页

作者： Zhu, Yinghui Jiang, Yuzhen Peng, Zhongxing Huang, Wei Hanshan Normal Univ Sch Comp & Informat Engn Qiaodong St Chaozhou 521041 Peoples R China

In semantic segmentation, category-hidden attack is a malicious adversarial attack which manipulates a specific category without affecting the recognition of other objects. A popular method is the nearest-neighbor algorithm, which modifies the segmentation map by replacing a target category with other categories close to it. Nearest-neighbor method aims to restrict the strength of perturbation noise that is imperceptive to both human eyes and segmentation algorithms. However, its spatial search adds lots of computational burden. In this paper, we propose two fast methods, dot-based method and line-based method, which are able to quickly complete the category transfers in logits maps without spatial search. The advantages of our two methods result from generating the logits maps by modifying the probability distribution of the category channels. Both of our methods are global, and the location and size of objects to hide are not cared, so their processing speed is very fast. The dot-based algorithm takes the pixel as the unit of calculation, and the line-based algorithm combines the category distribution characteristics of the horizontal direction to calculate. Experiments verify the effectiveness and efficiency compared with nearest-neighbor method. Specifically, in the segmentation map modification step, our methods are 5 times and 65 times faster than nearest-neighbor, respectively. In the small perturbation attack experiment, dot-based method gets the fastest speed, while different datasets and different setting experiments indicate that the line-based method is able to achieve faster and better adversarial segmentation results in most cases. (c) 2021 The Authors. Published by Atlantis Press B.v. This is an open access article distributed under the CC BY-NC 4.0 license (http://***/licenses/by-nc/4.0/).

关键词： Adversarial example Deep neural networks (DNNs) Semantic segmentation Logits map Category-hidden adversarial attack (CHAA)

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YOLO v3-Tiny: Object Detection and Recognition using one stage improved model 6

YOLO v3-Tiny: Object Detection and Recognition using one sta...

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6th International Conference on Advanced Computing and Communication systems (ICACCS)

作者： Adarsh, Pranav Rathi, Pratibha Kumar, Manoj Delhi Technol Univ Dept Comp Sci & Engn Delhi India

ISBN: (纸本)9781728151977;9781728151960

Object detection has seen many changes in algorithms to improve performance both on speed and accuracy. By the continuous effort of so many researchers, deep learning algorithms are growing rapidly with an improved object detection performance. various popular applications like pedestrian detection, medical imaging, robotics, self-driving cars, face detection, etc. reduces the efforts of humans in many areas. Due to the vast field and various state-of-the-art algorithms, it is a tedious task to cover all at once. This paper presents the fundamental overview of object detection methods by including two classes of object detectors. In two stage detector covered algorithms are RCNN, Fast RCNN, and Faster RCNN, whereas in one stage detector YOLO v1, v2, v3, and SSD are covered. Two stage detectors focus more on accuracy, whereas the primary concern of one stage detectors is speed. We will explain an improved YOLO version called YOLO v3-Tiny, and then its comparison with previous methods for detection and recognition of object is described graphically.

关键词： Computer vision YOLO v3 Faster RCNN Deep learning YOLO v3-Tiny Object detection image processing Convolutional Neural Networks

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A non-intrusive approach for fault detection and diagnosis of water distribution systems based on image sensors, audio sensors and an inspection robot

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ENERGY AND BUILDINGS 2021年 243卷 110967-110967页

作者： He, Ruikai Xu, Peng Chen, Zhibo Luo, Wei Su, Zhineng Mao, Jiong Tongji Univ Dept Mech & Energy Engn Room A4344800 Caoan Rd Shanghai 201804 Peoples R China Shanghai Hongqiao Int Airport Shanghai 201804 Peoples R China Shanghai Dynawin Facil Management Co Ltd Shanghai 201804 Peoples R China

Fault diagnosis is important to maintain the normal operation of air-conditioning systems, reduce the energy consumption in buildings, and increase the service life of air-conditioning system equipment. We present a novel approach for fault detection and diagnosis system that relies on image and audio sensors and relevant algorithms. This paper proposes a fault diagnosis algorithm based on a robot that can automatically capture audio and image signals from microphone arrays and cameras during inspection in a chiller room. It includes audio-and image-based fault diagnosis algorithms. The validity of the algorithm combined with sensors is verified using data from actual equipment in a chiller room. The audio-based algorithm, which can monitor the abnormal sound of pumps to detect faults, utilizes Fourier transform, a finite impulse response digital filter, and an autoregressive integrated moving average model. We analyze the frequency domain of the pump signal and set the appropriate threshold to monitor abnormal signals based on the fitted model. Meanwhile, the image-based algorithms are divided into three sections to achieve three functions: 1) an AlexNet convolutional neural network is modified to classify the images of the chiller room equipment obtained by the visible light camera;2) image morphology methods and trigonometric functions are used to read the dials' indicators acquired by the visible light camera;and 3) optical character recognition is used to obtain the highest temperature value in the infrared image of the pump captured by the infrared camera, which helps maintenance staff verify the operation of the pump and detect faults as soon as possible. These diagnostic algorithms are non-intrusive, low cost, and easy to deploy. Combined with real-time data collection from the sensors on the robot, the algorithms can effectively improve the intelligence of the equipment room and allocate human resources more reasonably. (c) 2021 Elsevier B.v. All rights res

关键词： Fault diagnosis Audio signal processing image processing Inspection robot

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An efficient deep learning model to categorize brain tumor using reconstruction and fine-tuning

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EXPERT systems WITH APPLICATIONS 2023年第1期230卷

作者： Talukder, Md. Alamin Islam, Md. Manowarul Uddin, Md. Ashraf Akhter, Arnisha Pramanik, Md. Alamgir Jalil Aryal, Sunil Almoyad, Muhammad Ali Abdulllah Hasan, Khondokar Fida Moni, Mohammad Ali Jagannath Univ Dept Comp Sci & Engn Dhaka Bangladesh Deakin Univ Sch Informat Technol Geelong Waurn Ponds Campus Geelong Australia Rangpur Med Coll Hosp Dept Surg Rangpur India Khamis Mushyt King Khalid Univ Dept Basic Med Sci Coll Appl Med Sci 47 AbhaPOB 4536 Mushait 61412 Saudi Arabia Queensland Univ Technol QUT Sch Comp Sci 2 George St Brisbane 4000 Australia Univ Queensland St Lucia Fac Hlth & Behav Sci Sch Hlth & Rehabil Sci Artificial Intelligence & Data Sci St Lucia Qld 4072 Australia

Brain tumors are among the most fatal and devastating diseases, often resulting in significantly reduced life expectancy. An accurate diagnosis of brain tumors is crucial to devise treatment plans that can extend the lives of affected individuals. Manually identifying and analyzing large volumes of MRI data is both challenging and time-consuming. Consequently, there is a pressing need for a reliable deep learning (DL) model to accurately diagnose brain tumors. In this study, we propose a novel DL approach based on transfer learning to effectively classify brain tumors. Our novel method incorporates extensive pre-processing, transfer learning architecture reconstruction, and fine-tuning. We employ several transfer learning algorithms, including Xception, ResNet50v2, InceptionResNetv2, and DenseNet201. Our experiments used the Figshare MRI brain tumor dataset, comprising 3,064 images, and achieved accuracy scores of 99.40%, 99.68%, 99.36%, and 98.72% for Xception, ResNet50v2, InceptionResNetv2, and DenseNet201, respectively. Our findings reveal that ResNet50v2 achieves the highest accuracy rate of 99.68% on the Figshare MRI brain tumor dataset, outperforming existing models. Therefore, our proposed model's ability to accurately classify brain tumors in a short timeframe can aid neurologists and clinicians in making prompt and precise diagnostic decisions for brain tumor patients.

关键词： Neuroscience Deep learning Transfer learning Brain MRI image Brain tumor Classification

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Ingenious Waste Management: Implementing Machine Learning in Waste Segregation

Ingenious Waste Management: Implementing Machine Learning in...

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Emerging Smart Computing and Informatics (ESCI), Conference on

作者： Asha v B Nithya Govindaraj M Brunda T M Ashwini Hiremath B Jayaprakash New Horizon College of Engineering Bengaluru India

Waste management is a pressing global issue, and the need for efficient waste separation processes is becoming increasingly important. Incorporating Machine Learning techniques with waste separation has yielded promising results and revolutionized the waste separation process. However, this research paper highlights the gaps that exist in the understanding and efficacy of machine learning for waste separation. This study provides an overview of state-of-the-art techniques for waste separation using machine learning, including the use of computer vision, sensor technology, and neural networks. This paper portrays case studies with empirical data from various waste management establishments to demonstrate the potential advantages of machine learning for recycling and waste classifying. Although progress has been made, this article highlights some significant gaps in our knowledge. These gaps include the need for standardized datasets, optimization of algorithms for real-world scenarios, the impact of technology on the environment, and the socio-economic aspects of integrating automation into waste management. The essential and most important tasks of intelligent mechanisms in the digital age is waste separation. Segregation of waste is necessary to apply proper disposal and waste management strategies. The existing systems use drones to identify waste by using deep learning, image processing, GPS, and GSM approaches, locating and notifying the authorities about them. The improvement made is in the analysis and utilize multi-object detection and picture classification to accomplish trash segregation. Waste management may be effectively accomplished with higher accuracy through Machine Learning practices which therefore considerably lowers labor expenses.

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v2X Communications for Platooning: Impact of Sensor Inaccuracy

V2X Communications for Platooning: Impact of Sensor Inaccura...

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11th International image processing and Communications Conference (IP and C)

作者： Sybis, Michal Sroka, Pawel Kliks, Adrian Kryszkiewicz, Pawel Poznan Univ Tech Pl M Sklodowskiej Curie 5 PL-60965 Poznan Poland

ISBN: (纸本)9783030312541;9783030312534

Platooning is one of the prospective applications, where autonomous driving and vehicle-to-vehicle and vehicle-to-Infrastructure communications play a vital role. various test proved that reduction of inter-car distance withing platoon of trucks results in significant fuel savings. Keeping of such short distance as well as awareness of the platooning cars about the vicinity assumes the presence of highly reliable sensors. In this paper, we briefly discuss the impact of sensor inaccuracy on the overall behaviour of the train-of-trucks driving in the autonomous fashion, supported by the presence of dedicated wireless system utilizing context information stored in databases and maps.

关键词： v2X and 5G Sensor accuracy Context information database

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Development of a new hybrid cloud cover detection method for all sky mapping

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ASTRONOMY AND COMPUTING 2022年 38卷 100551-100551页

作者： Akdemir, Kemal Unal Dindar, Murat Senel, Fatih Ahmet Helhel, Selcuk Akdeniz Univ Engn Fac Dept Elect & Elect Engn Antalya Turkey TUBITAK Natl Observ Akdeniz Univ Campus Antalya Turkey Suleyman Demirel Univ Engn Fac Dept Comp Engn Isparta Turkey

Local precise cloud detection techniques in the daytime are finding a wide range of applications in solar farms and for tracking the factory flue gases. Additionally, the cloud coverage is a measure for the site quality of astronomical observatories. The moon is itself a problem for astronomical observations. If there are astronomical objects near to moon, the light scattered by clouds from moon in the sky also has to be determined to extract from the observation schedule of robotic telescope. A novel hybrid method to deal with all-sky images in any conditions in daytime and nighttime is developed, and related algorithms are implemented in MATLAB. A GUI has been presented which interactively analyzes the images taken from all-sky cams. It plots a radar scan of the sky with clouds in polar coordinate systems. The systematic mean error is estimated at less than 3%. (c) 2022 Elsevier B.v. All rights reserved.

关键词： All-sky-camera Observatory Cloudiness-analysis image processing

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