Naina Nimisha, Subrat Pandey, Siddhant Priyadarshi, Dr. Anusha Preetham
{"title":"道路感知：智能道路监控系统","authors":"Naina Nimisha, Subrat Pandey, Siddhant Priyadarshi, Dr. Anusha Preetham","doi":"10.22214/ijraset.2024.63499","DOIUrl":null,"url":null,"abstract":"Abstract: This project proposes a system for detecting traffic signals, lane layouts, and speed bumps in road infrastructure using video footage through machine learning. Lane detection is performed through region of interest selection and edge detection. Lane lines are extracted based on specific characteristics. A deep learning model is trained to detect lane boundaries and road curvature. The system provides real-time alerts and recommendations to enhance road safety and driving experiences.","PeriodicalId":13718,"journal":{"name":"International Journal for Research in Applied Science and Engineering Technology","volume":"44 12","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Road Sense: Intelligent Road Monitoring System\",\"authors\":\"Naina Nimisha, Subrat Pandey, Siddhant Priyadarshi, Dr. Anusha Preetham\",\"doi\":\"10.22214/ijraset.2024.63499\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract: This project proposes a system for detecting traffic signals, lane layouts, and speed bumps in road infrastructure using video footage through machine learning. Lane detection is performed through region of interest selection and edge detection. Lane lines are extracted based on specific characteristics. A deep learning model is trained to detect lane boundaries and road curvature. The system provides real-time alerts and recommendations to enhance road safety and driving experiences.\",\"PeriodicalId\":13718,\"journal\":{\"name\":\"International Journal for Research in Applied Science and Engineering Technology\",\"volume\":\"44 12\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-07-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal for Research in Applied Science and Engineering Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.22214/ijraset.2024.63499\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal for Research in Applied Science and Engineering Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.22214/ijraset.2024.63499","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Abstract: This project proposes a system for detecting traffic signals, lane layouts, and speed bumps in road infrastructure using video footage through machine learning. Lane detection is performed through region of interest selection and edge detection. Lane lines are extracted based on specific characteristics. A deep learning model is trained to detect lane boundaries and road curvature. The system provides real-time alerts and recommendations to enhance road safety and driving experiences.
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