{"title":"基于深度学习的火灾预警算法研究与实现","authors":"Xue Li, Song Wang, Zheng Yu, T. Liu, Hong Yan","doi":"10.1109/ISAIAM55748.2022.00017","DOIUrl":null,"url":null,"abstract":"For residential buildings, factories, gas stations, roads, forests and other fire scenarios with high incidence, intelligent fire prediction and early warning system will be realized by using cloud computing, Internet of Things computing, communication technology, artificial intelligence and other technologies to improve the intelligent level of fire prevention. This project intends to use the object detection technology of flying oar deep learning to automatically detect smoke and fire in the monitoring area and help relevant personnel to deal with it in time, so as to minimize casualties and property losses. This study can help users solve practical problems in fire and smoke detection applications more efficiently.","PeriodicalId":382895,"journal":{"name":"2022 2nd International Symposium on Artificial Intelligence and its Application on Media (ISAIAM)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Research and Implementation of Fire Warning Algorithm Based on Deep Learning\",\"authors\":\"Xue Li, Song Wang, Zheng Yu, T. Liu, Hong Yan\",\"doi\":\"10.1109/ISAIAM55748.2022.00017\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"For residential buildings, factories, gas stations, roads, forests and other fire scenarios with high incidence, intelligent fire prediction and early warning system will be realized by using cloud computing, Internet of Things computing, communication technology, artificial intelligence and other technologies to improve the intelligent level of fire prevention. This project intends to use the object detection technology of flying oar deep learning to automatically detect smoke and fire in the monitoring area and help relevant personnel to deal with it in time, so as to minimize casualties and property losses. This study can help users solve practical problems in fire and smoke detection applications more efficiently.\",\"PeriodicalId\":382895,\"journal\":{\"name\":\"2022 2nd International Symposium on Artificial Intelligence and its Application on Media (ISAIAM)\",\"volume\":\"58 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 2nd International Symposium on Artificial Intelligence and its Application on Media (ISAIAM)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISAIAM55748.2022.00017\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 2nd International Symposium on Artificial Intelligence and its Application on Media (ISAIAM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISAIAM55748.2022.00017","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Research and Implementation of Fire Warning Algorithm Based on Deep Learning
For residential buildings, factories, gas stations, roads, forests and other fire scenarios with high incidence, intelligent fire prediction and early warning system will be realized by using cloud computing, Internet of Things computing, communication technology, artificial intelligence and other technologies to improve the intelligent level of fire prevention. This project intends to use the object detection technology of flying oar deep learning to automatically detect smoke and fire in the monitoring area and help relevant personnel to deal with it in time, so as to minimize casualties and property losses. This study can help users solve practical problems in fire and smoke detection applications more efficiently.