利用深度学习检测被盗车辆的车牌识别

Atul B. Kathole, Ajim Shikalgar, Nitish Supe, Tejasha Patil
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引用次数: 0

摘要

预计在不久的将来,印度将超过中国,成为第三大汽车市场。数据显示,车辆盗窃每年都在增加。但警方真正解决的案件比例仍然很小。警方很难找到被盗车辆,因为它们有时被带到远离盗窃现场的地方。因此,需要一个自动化系统来协助跟踪这类车辆。这些问题正是我们的项目试图解决的。警方将从这个系统中获得大量的信息,他们可以利用这些信息来解决盗窃案件。该识别系统采用YOLO V3算法和Canny边缘检测,实现车牌号码的自动识别。车牌识别完成后,处理步骤如下:1.单击“确定”。给车牌拍照。2. 识别和区分字符。3.然后,时间和日期与识别车牌一起记录在数据库中以供进一步使用。4. 如果发现了被盗车辆,则准备一份详细的报告,详细说明车辆首次出现的地点和时间,并通知警方已经找到了匹配的车辆。该方法可用于提高安全性和准确性。
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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.
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