Atul B. Kathole, Ajim Shikalgar, Nitish Supe, Tejasha Patil
{"title":"利用深度学习检测被盗车辆的车牌识别","authors":"Atul B. Kathole, Ajim Shikalgar, Nitish Supe, Tejasha Patil","doi":"10.1109/I2CT57861.2023.10126393","DOIUrl":null,"url":null,"abstract":"India is anticipated to overtake China as the third-largest vehicle market in the near future. Vehicle theft, according to data, has increased yearly. But the proportion of cases that the police really resolve is still quite small. It is challenging for police to locate stolen vehicles since they are sometimes carried to locations distant from the scene of the theft. Therefore, a need for an automated system to assist in tracking such cars arises. These issues are what our project tries to fix. The police will receive a tonne of information from this system that they may utilise to solve theft cases. Using the YOLO V3 algorithm and Canny Edge Detection, the identification system will automatically recognize automobile license plate numbers. After a license plate is identified, the following actions are taken: 1. to photograph the license plate. 2. to recognize and divide characters. 3. The time and date are then recorded in a database together with the identifying license plate for further use. 4. In the event that a stolen vehicle is discovered, a thorough report detailing the location and the time the vehicle first appeared is prepared, and police are notified that a match has been made. The method may be applied to increase security and accuracy.","PeriodicalId":150346,"journal":{"name":"2023 IEEE 8th International Conference for Convergence in Technology (I2CT)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"License Plate Recognition for Detecting Stolen Vehicle Using Deep Learning\",\"authors\":\"Atul B. Kathole, Ajim Shikalgar, Nitish Supe, Tejasha Patil\",\"doi\":\"10.1109/I2CT57861.2023.10126393\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"India is anticipated to overtake China as the third-largest vehicle market in the near future. Vehicle theft, according to data, has increased yearly. But the proportion of cases that the police really resolve is still quite small. It is challenging for police to locate stolen vehicles since they are sometimes carried to locations distant from the scene of the theft. Therefore, a need for an automated system to assist in tracking such cars arises. These issues are what our project tries to fix. The police will receive a tonne of information from this system that they may utilise to solve theft cases. Using the YOLO V3 algorithm and Canny Edge Detection, the identification system will automatically recognize automobile license plate numbers. After a license plate is identified, the following actions are taken: 1. to photograph the license plate. 2. to recognize and divide characters. 3. The time and date are then recorded in a database together with the identifying license plate for further use. 4. In the event that a stolen vehicle is discovered, a thorough report detailing the location and the time the vehicle first appeared is prepared, and police are notified that a match has been made. The method may be applied to increase security and accuracy.\",\"PeriodicalId\":150346,\"journal\":{\"name\":\"2023 IEEE 8th International Conference for Convergence in Technology (I2CT)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-04-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2023 IEEE 8th International Conference for Convergence in Technology (I2CT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/I2CT57861.2023.10126393\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 IEEE 8th International Conference for Convergence in Technology (I2CT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/I2CT57861.2023.10126393","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
License Plate Recognition for Detecting Stolen Vehicle Using Deep Learning
India is anticipated to overtake China as the third-largest vehicle market in the near future. Vehicle theft, according to data, has increased yearly. But the proportion of cases that the police really resolve is still quite small. It is challenging for police to locate stolen vehicles since they are sometimes carried to locations distant from the scene of the theft. Therefore, a need for an automated system to assist in tracking such cars arises. These issues are what our project tries to fix. The police will receive a tonne of information from this system that they may utilise to solve theft cases. Using the YOLO V3 algorithm and Canny Edge Detection, the identification system will automatically recognize automobile license plate numbers. After a license plate is identified, the following actions are taken: 1. to photograph the license plate. 2. to recognize and divide characters. 3. The time and date are then recorded in a database together with the identifying license plate for further use. 4. In the event that a stolen vehicle is discovered, a thorough report detailing the location and the time the vehicle first appeared is prepared, and police are notified that a match has been made. The method may be applied to increase security and accuracy.