J. R. Smit, Hugh G. P. Hunt, C. Schumann, T. Warner
{"title":"基于机器学习的高速闪电镜头语义分段度量集","authors":"J. R. Smit, Hugh G. P. Hunt, C. Schumann, T. Warner","doi":"10.1109/ICLPandSIPDA54065.2021.9627429","DOIUrl":null,"url":null,"abstract":"An investigation of the accuracy of a system using semantic segmentation in determining the number of individual strokes, direction of leaders and strike points for high-speed lightning footage. The paper uses a pre-trained DeepLabv3+ network, chosen to allow for the lowest computer requirements, where the last layers of the model are retrained on lightning footage. The network produces semantic segmented images where each pixel has a numerical label which are evaluated to count strokes. Regions of interest are created per strike and used to reduce noise. The system has a stroke detection efficiency of 70.1%, direction accuracy of 80% and a strike point accuracy of 89.5% when evaluated on 15 videos.","PeriodicalId":70714,"journal":{"name":"中国防雷","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2021-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Metric collection on semantically segmented highspeed lightning footage with machine learning\",\"authors\":\"J. R. Smit, Hugh G. P. Hunt, C. Schumann, T. Warner\",\"doi\":\"10.1109/ICLPandSIPDA54065.2021.9627429\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"An investigation of the accuracy of a system using semantic segmentation in determining the number of individual strokes, direction of leaders and strike points for high-speed lightning footage. The paper uses a pre-trained DeepLabv3+ network, chosen to allow for the lowest computer requirements, where the last layers of the model are retrained on lightning footage. The network produces semantic segmented images where each pixel has a numerical label which are evaluated to count strokes. Regions of interest are created per strike and used to reduce noise. The system has a stroke detection efficiency of 70.1%, direction accuracy of 80% and a strike point accuracy of 89.5% when evaluated on 15 videos.\",\"PeriodicalId\":70714,\"journal\":{\"name\":\"中国防雷\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-09-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"中国防雷\",\"FirstCategoryId\":\"1089\",\"ListUrlMain\":\"https://doi.org/10.1109/ICLPandSIPDA54065.2021.9627429\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"中国防雷","FirstCategoryId":"1089","ListUrlMain":"https://doi.org/10.1109/ICLPandSIPDA54065.2021.9627429","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Metric collection on semantically segmented highspeed lightning footage with machine learning
An investigation of the accuracy of a system using semantic segmentation in determining the number of individual strokes, direction of leaders and strike points for high-speed lightning footage. The paper uses a pre-trained DeepLabv3+ network, chosen to allow for the lowest computer requirements, where the last layers of the model are retrained on lightning footage. The network produces semantic segmented images where each pixel has a numerical label which are evaluated to count strokes. Regions of interest are created per strike and used to reduce noise. The system has a stroke detection efficiency of 70.1%, direction accuracy of 80% and a strike point accuracy of 89.5% when evaluated on 15 videos.