{"title":"基于深度神经网络和图像处理的火灾探测新方法","authors":"Yuning Wang","doi":"10.1109/INSAI56792.2022.00022","DOIUrl":null,"url":null,"abstract":"The development of computer vision and deep neural networks has enabled the accurate classification of objects like fire. Fire can pose a serious threat to humans, so the prevention of fire becomes a major concern for society. In this paper, a trained ResNet-50 model is combined with an RGB-based analysis of fire to achieve higher accuracy. In short, the ResNet-50 model categorizes images quickly, while the RGB model can be adjusted based on the actual environment. They complement each other. In images, the RGB model correctly detects the fire, whereas the ResNet-50 model achieves 96.2% accuracy.","PeriodicalId":318264,"journal":{"name":"2022 2nd International Conference on Networking Systems of AI (INSAI)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Novel Fire Detection Method Based on Deep Neural Network and Image Processing\",\"authors\":\"Yuning Wang\",\"doi\":\"10.1109/INSAI56792.2022.00022\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The development of computer vision and deep neural networks has enabled the accurate classification of objects like fire. Fire can pose a serious threat to humans, so the prevention of fire becomes a major concern for society. In this paper, a trained ResNet-50 model is combined with an RGB-based analysis of fire to achieve higher accuracy. In short, the ResNet-50 model categorizes images quickly, while the RGB model can be adjusted based on the actual environment. They complement each other. In images, the RGB model correctly detects the fire, whereas the ResNet-50 model achieves 96.2% accuracy.\",\"PeriodicalId\":318264,\"journal\":{\"name\":\"2022 2nd International Conference on Networking Systems of AI (INSAI)\",\"volume\":\"15 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 2nd International Conference on Networking Systems of AI (INSAI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/INSAI56792.2022.00022\",\"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 Conference on Networking Systems of AI (INSAI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/INSAI56792.2022.00022","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Novel Fire Detection Method Based on Deep Neural Network and Image Processing
The development of computer vision and deep neural networks has enabled the accurate classification of objects like fire. Fire can pose a serious threat to humans, so the prevention of fire becomes a major concern for society. In this paper, a trained ResNet-50 model is combined with an RGB-based analysis of fire to achieve higher accuracy. In short, the ResNet-50 model categorizes images quickly, while the RGB model can be adjusted based on the actual environment. They complement each other. In images, the RGB model correctly detects the fire, whereas the ResNet-50 model achieves 96.2% accuracy.