A Deep Real-Time Fire Prediction Parallel D-CNN Model on UDOO BOLT V8

作     者：Amal H.Alharbi Hanan A.Hosni Mahmoud 

作者机构：Department of Computer SciencesCollege of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2022年第73卷第12期

页      面：6237-6252页

核心收录：

学科分类：0710[理学-生物学] 0401[教育学-教育学] 04[教育学] 0907[农学-林学] 0837[工学-安全科学与工程] 0802[工学-机械工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2022R120) Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia 

主　　题：Fire prediction UDOO BOLT V8 deep learning 

摘      要：Hazardous incidences have significant influences on human life,and fire is one of the foremost causes of such hazard in most *** prediction and classification model from a set of fire images can decrease the risk of losing human lives and *** promotion of fire emergency can be of great ***,construction of these prediction models is relevant and *** article proposes an operative fire prediction model that depends on a prediction unit embedded in the processor UDOO BOLT V8 hardware to predict fires in real time.A fire image database is improved to enhance the images quality prior to classify them as either fire or *** proposed deep learning-based Very Deep Convolutional Networks Visual Geometry Group(VGG-16)model(Parallel VGG-16)is an enhanced version of the VGG-16 model,by incorporating parallel convolution layers and a fusion module for better *** experimental results validate the performance of the Parallel VGG-16 which achieves an accuracy of 97%,compared to the compared state-of-the-art ***,we integrate the prediction module into a UDOO BOLT V8 computer,which precisely controlled the fire alarm so that it can cautious people from fire in real *** this paper we propose a complete fire prediction model using a camera and the UDOO BOLT V8 embedded *** experiments validate the effectiveness and applicability of the proposed fire model.