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Efficient Deep Learning Framework for Fire Detection in Complex Surveillance Environment

Computer Systems Science & Engineering 2023年第7期46卷 749-764页

作者： Naqqash Dilshad Taimoor Khan JaeSeung Song Department of Convergence Engineering for Intelligent Drone Seoul05006Korea Department of Computer Science Islamia College PeshawarPeshawar25120Pakistan

To prevent economic,social,and ecological damage,fire detection and management at an early stage are significant yet *** computationally complex networks have been developed,attention has been largely focused on improving accuracy,rather than focusing on real-time fire ***,in this study,the authors present an efficient fire detection framework termed E-FireNet for real-time detection in a complex surveillance *** proposed model architecture is inspired by the VGG16 network,with significant modifications including the entire removal of Block-5 and tweaking of the convolutional layers of *** results in higher performance with a reduced number of parameters and inference ***,smaller convolutional kernels are utilized,which are particularly designed to obtain the optimal details from input images,with numerous channels to assist in feature *** E-FireNet,three steps are involved:preprocessing of collected data,detection of fires using the proposed technique,and,if there is a fire,alarms are generated and transmitted to law enforcement,healthcare,and management ***,E-FireNet achieves 0.98 accuracy,1 precision,0.99 recall,and 0.99 F1-score.A comprehensive investigation of various Convolutional Neural Network(CNN)models is conducted using the newly created Fire Surveillance SV-Fire *** empirical results and comparison of numerous parameters establish that the proposed model shows convincing performance in terms of accuracy,model size,and execution time.

关键词： Deep learning drone embedded vision emergency monitoring fire classification fire detection IoT search and rescue

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Visual Measurement and Control for Underwater Robots: A Survey

Visual Measurement and Control for Underwater Robots: A Surv...

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第25届中国控制与决策会议

作者： Feihu Sun Junzhi Yu De Xu State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences The Research Center of Precision Sensing and Control Institute of Automation Chinese Academy of Sciences

ISBN: (纸本)9781467355339

The recently increased interests in visual measurement and control applied to underwater robots have challenged the existing visual technologies that once were sufficient in ground-based applications. This paper surveys the major difficulties and representative applications in underwater visual measurement and control, most of which result from the particularity of the aquatic environments. Specifically, the state of the art of the underwater vision is summarized from different perspectives, primarily involving key points and existing problems. In particular, the application of the embedded vision-based system in the context of bionics is accented. Finally, the potential development trends and possible breakthroughs are discussed, shedding light on the effort both in hardware and software to advance real-world underwater vision applications.

关键词： Visual measurement and control Underwater robots embedded vision Bio-robotics.

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Towards Sustainable Agricultural Systems:A Lightweight Deep Learning Model for Plant Disease Detection

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Computer Systems Science & Engineering 2023年第10期47卷 515-536页

作者： Sana Parez Naqqash Dilshad Turki M.Alanazi Jong Weon Lee Department of Software Sejong UniversitySeoul05006Korea Department of Convergence Engineering for Intelligent Drone Sejong UniversitySeoul05006Korea Department of Electrical Engineering College of EngineeringJouf UniversitySakakaSaudi Arabia

A country’s economy heavily depends on agricultural ***,due to several plant diseases,crop growth rate and quality are highly *** identification of these diseases via a manual procedure is very challenging and time-consuming because of the deficiency of domain experts and low-contrast ***,the agricultural management system is searching for an automatic early disease detection *** this end,an efficient and lightweight Deep Learning(DL)-based framework(E-GreenNet)is proposed to overcome these problems and precisely classify the various *** the end-to-end architecture,a MobileNetV3Smallmodel is utilized as a backbone that generates refined,discriminative,and prominent ***,the proposed model is trained over the PlantVillage(PV),Data Repository of Leaf Images(DRLI),and a new Plant Composite(PC)dataset individually,and later on test samples,its actual performance is *** extensive experimental analysis,the proposed model obtained 1.00%,0.96%and 0.99%accuracies on all three included ***,the proposed method achieves better inference speed when compared with other State-Of-The-Art(SOTA)*** addition,a comparative analysis is conducted where the proposed strategy shows tremendous discriminative scores as compared to the various pretrained models and other Machine Learning(ML)and DL methods.

关键词： Computer vision deep learning embedded vision agriculture monitoring classification plant disease detection Internet of Things(IoT)

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