Here are a few use cases for this project:

1. Vehicle Identification and Tracking: This use case involves leveraging the 'licenseplate' model to identify and monitor vehicle movements in certain areas such as parking lots, city streets, or highways. Identified license plates would be stored in a database for future analysis and tracking purposes.

2. Traffic Rule Enforcement: This application can automate the process of monitoring traffic rules. The model could be used to detect cars and read their license plates, further automating the process of issuing tickets for violations like speed overruns or illegal parking.

3. Toll Collection: Automated toll booths could use this model to read license plates of approaching vehicles, instantly billing the owners or flagging unregistered vehicles without human intervention.

4. Stolen Vehicle Recovery: The 'licenseplate' model can be used in surveillance systems to detect stolen vehicles by continuously scanning parked or passing cars and matching the license plates with a database of reported stolen vehicles.

5. Car Rental Service Management: Car rental companies could use this model to automate the check-in/check-out process. A camera-enabled system would identify the license plate, linking it to a particular reservation, and automatically update the rental status in the system.

This is a listing of the vehicles that were towed in the past 30 days and it is updated hourly. It shows vehicle information, license plate number, and where the vehicle was towed along with other information. When there is a large volume of towing in will take a while for the information to be updated. This data set does not include vehicles coded in the database as evidence, operator arrested, suspected stolen, or all holds.

Here are a few use cases for this project:

1. Vehicle Identification Number (VIN) Decoder: Create an application that uses the "vin-detection_letters_2" model to decode and provide details about vehicles based on their VINs. Users can simply take a photo of a vehicle's VIN, and the application will identify the letters, look up vehicle information, and then display the results.

2. Automotive Maintenance and Repair: Integrate the "vin-detection_letters_2" model into maintenance or repair shop software. The model can help identify the VINs from photos taken by mechanics or customers, providing accurate and efficient vehicle information needed for diagnostics or repairs.

3. Vehicle Tracking and Management: Use the "vin-detection_letters_2" model in fleet management, law enforcement, or parking enforcement applications to identify vehicles and automate processes like asset management, vehicle registration, or issuing parking violations.

4. Vehicle Sales and Registration: Implement the "vin-detection_letters_2" model in vehicle sales platforms or government registration systems to simplify the vehicle identification, registration, and documentation processes. Users can easily input relevant details by taking a photo of the VIN, improving the accuracy and speed of the process.

5. Stolen Vehicle Recovery: Employ the "vin-detection_letters_2" model in security camera systems or by law enforcement agencies to identify stolen vehicles. When a VIN is captured by the model, the system can cross-reference it with a database of stolen vehicles, potentially aiding in the recovery of the stolen property.

There were ******* motor vehicle thefts in England and Wales in 2023/24, compared with ******* in the previous reporting year. Despite recent increases in this type of offence, there were still far fewer vehicle thefts than there were in 2002/03 when there were almost *******. This was followed by a steep ten-year decline which saw vehicle thefts reduced to just ****** in 2013/14. Links with overall crime The sharp fall in motor vehicle thefts seen between 2002/03 and the mid-2010s, followed by a sudden increase recently tracks a pattern that can be observed in the overall crime figures for the United Kingdom In total, there were approximately **** million crime offences in 2023/24, an increase of over *** million offences when compared with 2013/14. Although this was a higher number of crimes than in the early 2000s, due to population increase, the crime rate for 2023/24 was ****, lower than in 2003/04, when the crime rate was ***** crimes per 1,000 people. Staff and funding cuts to blame? The recent uptick in overall crime has been sudden and severe enough to catch the attention of the British media. It has not gone unnoticed that this rise occurred following cuts to funding for the police which was then followed by a decline in officer numbers These cuts have since been reversed, and funding for the police has again started to increase, although in other areas of the justice system, such as legal aid, funding has remained at reduced levels, when compared with spending before the mid-2010s.

Here are a few use cases for this project:

1. Traffic Management Application: The computer vision model could be used in monitoring and managing city traffic. It would be helpful to track vehicles and license plates, especially in identifying vehicles involved in violations such as speeding or running red lights.

2. Parking Lot Management: The model can be used in automated parking systems to identify and record the number plates of vehicles entering or exiting a parking area. This would help in tracking parking time and calculating parking fees, and also ensure security by keeping record of all vehicles using the space.

3. Security and Surveillance: For both public and private security purposes, YOLO can be used to monitor vehicles entering a specified area. For instance, in gated communities, commercial complexes, or government buildings, it can provide real-time updates of all vehicles coming in and going out.

4. Auto Theft Detection: Law enforcement and security agencies can utilize this computer vision model to monitor and identify stolen vehicles. By integrating it with a database of stolen vehicle license plates, it could identify when a stolen vehicle is in a given area.

5. Toll Collection: This model can be used by highway toll collection systems to automate the process of identifying vehicle number plates, thus speeding up the process and increasing the overall efficiency of toll collection.

Here are a few use cases for this project:

1. Traffic Enforcement: The "plates detection" model can be used to automatically detect and identify license plates of vehicles that violate traffic rules such as speeding, illegal parking, or running red lights. This information can be used by law enforcement to issue tickets and fines.

2. Parking Management: The model can be integrated into parking systems to automatically recognize license plates, allowing for efficient management of parking lots, tracking of vehicle usage, and automated billing for paid parking spaces.

3. Stolen Vehicle Recovery: The "plates detection" model can be employed in surveillance systems and traffic cameras to identify stolen vehicles by matching license plates against a database of reported stolen vehicles, thereby aiding law enforcement in vehicle recovery.

4. Access Control: The model can be utilized in restricted access areas, such as gated communities or company premises, to automatically grant or deny access to vehicles based on the license plate recognition.

5. Congestion Charging: The "plates detection" system can be implemented in cities with congestion charging schemes to identify vehicles entering and leaving congestion zones, enabling automated charging for road usage and supporting traffic reduction initiatives.

This dataset contains Crime and Safety data from the Cary Police Department.

This data is extracted by the Town of Cary's Police Department's RMS application. The police incidents will provide data on the Part I crimes of arson, motor vehicle thefts, larcenies, burglaries, aggravated assaults, robberies and homicides. Sexual assaults and crimes involving juveniles will not appear to help protect the identities of victims.

This dataset includes criminal offenses in the Town of Cary for the previous 10 calendar years plus the current year. The data is based on the National Incident Based Reporting System (NIBRS) which includes all victims of person crimes and all crimes within an incident. The data is dynamic, which allows for additions, deletions and/or modifications at any time, resulting in more accurate information in the database. Due to continuous data entry, the number of records in subsequent extractions are subject to change. Crime data is updated daily however, incidents may be up to three days old before they first appear.

About Crime Data

The Cary Police Department strives to make crime data as accurate as possible, but there is no avoiding the introduction of errors into this process, which relies on data furnished by many people and that cannot always be verified. Data on this site are updated daily, adding new incidents and updating existing data with information gathered through the investigative process.

This dynamic nature of crime data means that content provided here today will probably differ from content provided a week from now. Additional, content provided on this site may differ somewhat from crime statistics published elsewhere by other media outlets, even though they draw from the same database.

Withheld Data

In accordance with legal restrictions against identifying sexual assault and child abuse victims and juvenile perpetrators, victims, and witnesses of certain crimes, this site includes the following precautionary measures: (a) Addresses of sexual assaults are not included. (b) Child abuse cases, and other crimes which by their nature involve juveniles, or which the reports indicate involve juveniles as victims, suspects, or witnesses, are not reported at all.

Certain crimes that are under current investigation may be omitted from the results in avoid comprising the investigative process.

Incidents five days old or newer may not be included until the internal audit process has been completed.

This data is updated daily.

Here are a few use cases for this project:

1. Parking Management System: Integrating the model with CCTV cameras in public and private parking areas to automatically detect and log number plates. This aids in monitoring parking time, billing, security, and identifying unauthorized vehicles.

2. Traffic Management and Law Enforcement: Used in monitoring traffic and identifying vehicles violating rules. The model could help identify and track the license number of speeding vehicles, illegal parking, or those involved in a hit and run.

3. Toll Booth Automation: Implementing the model at toll booths for automatic toll collection from vehicles by identifying their number plate, reducing the need for manual intervention and long waiting times.

4. Security and Surveillance: The model could be used in residential and commercial areas' security to monitor vehicles' entry and exit. It can aid in identifying any suspicious activity or unauthorized intrusion.

5. Vehicle Theft Detection: Can be used by law enforcement agencies to identify stolen vehicles by scanning large amounts of traffic using CCTV footage and cross-checking it with the database of stolen vehicles.

Here are a few use cases for this project:

1. Automatic License Plate Recognition: The LP Reader model can be used in security and law enforcement applications to automatically recognize and identify license plates. By comparing this information with a database, it can aid in vehicle tracking or locating stolen vehicles.

2. Parking Management Systems: This system can assist in the automation of parking facilities by recognizing and logging license plates as cars enter and exit, without requiring physical tickets or human intervention.

3. Traffic regulation and enforcement: By identifying vehicle license plates, the model could be used in traffic control systems to detect vehicles that have violated traffic rules such as speeding, illegal parking, or running red lights.

4. Toll Collection: Automatic identification of license plates could be used in a toll collection system, eliminating the need for manual labor and speeding up the process.

5. Enhanced CCTV systems: Integrating this model within CCTV systems can provide additional functionality, like tracking specific vehicles based on their license plates in situations like shopping malls or private property surveillance.

English

The "Taiwan License Plate Character Recognition Research" project focuses on identifying characters primarily based on Taiwan license plate fonts, coupled with license plate detection technology. Through our simple yet practical code, users can assemble a full license plate number according to the X-coordinate of the characters. The aim of this project is to optimize the license plate recognition process, enabling a faster, more accurate capture of license plate numbers.

Generate by GPT4 Here are a few use cases for this project:

1. Automated Parking System: Utilize the "High-precision Taiwan license plate number recognition" model to read and recognize license plates in parking lots, allowing for streamlined and automated entry/exit management and billing.

2. Traffic Surveillance and Enforcement: Integrate the model into traffic monitoring systems to identify traffic violators, such as speeding or running red lights, by capturing and recognizing their license plates, and assist law enforcement in issuing fines or citations.

3. Stolen Vehicle Detection: Leverage the model within police and security systems to identify stolen or flagged vehicles by matching their license plates in real-time with a database of reported stolen or wanted vehicles.

4. Intelligent Transportation System: Incorporate the model into smart city infrastructure for monitoring and predicting traffic flow, analyzing road conditions, and managing traffic signals, based on real-time vehicle count and license-plate identification.

5. Access Control and Security: Implement the model in gated communities, corporate campuses, or sensitive facilities to provide automated access control to authorized vehicles, enhancing security and convenience for residents, employees, and visitors.

Additional Explanation: The images in this project come from multiple different authors' projects. Prior to the creation of this dataset, we performed the following steps on the images:

1. Detected the presence of license plates.
2. Centered and cropped the images around the license plates to reduce training time and maintain the proportion of license plate fonts.
3. Manually inspected the clarity of the license plate letters.
4. Completed preliminary annotations.
5. Conducted semi-automatic annotations using a smaller initial model.
6. Reviewed each image individually.

However, due to the complexity of annotating license plate fonts and the high requirements for image quality, we have only annotated over 700 images with our limited manpower. Notably, each image requires at least seven letters to be marked.

If you have other questions or want to discuss this data set, you can contact: https://t.me/jtx257

注意事項

• 缺點：檢測信心較低。由於我沒有更多時間幫助這個項目，如果您願意幫助我們進行標記，請聯絡我(https://t.me/jtx257)，我們將樂於提供其他技術協助。
• 優勢：圖像精細裁剪。

台灣車牌字元識別研究

英文

High-precision Taiwan license plate number recognition專案主要聚焦於識別基於台灣車牌字體的字元，結合車牌檢測技術。通過我們簡潔實用的程式碼，用戶可以根據字元的X坐標組合出完整的車牌號碼。此項目旨在優化車牌識別過程，使其更快速、準確地捕捉車牌號碼。

由GPT4生成 以下是此項目的幾個**應用案例**：

1. 自動停車系統：利用“台灣車牌字元識別研究”模型，在停車場讀取和識別車牌，從而實現出入口管理和計費的自動化。

2. 交通監控與執法：將模型整合到交通監控系統中，識別違反交通規則的行為，如超速或闖紅燈，通過捕捉並識別其車牌，協助執法部門開出罰單或傳票。

3. 被盜車輛檢測：在警方和安全系統中利用該模型，通過與報告中被盜或通緝車輛的數據庫即時匹配其車牌，識別被盜或被標記的車輛。

4. 智能交通系統：將模型納入智慧城市基礎設施，基於實時車輛計數和車牌識別，用於監測和預測交通流量，分析道路條件，並管理交通信號。

5. 出入控制與安全：在封閉社區、企業園區或敏感設施中實施該模型，為授權車輛提供自動出入控制，提升居民、員工和訪客的安全性和便利性。

額外說明: 該專案的圖片來自多個不同作者的專案。在製作這個資料集之前，我們已經對照片進行了以下幾個步驟:

1. 檢測車牌存在。
2. 以車牌為中心點，切割圖片，旨在降低訓練時間，並保留車牌字型的比例。
3. 進行人眼檢查，確認車牌字母是否清晰。
4. 進行初步的標註。
5. 利用較小的初始模型進行半自動標註。
6. 對每一張照片進行逐一檢查。

然而，由於車牌字型的標註難度較大，並且對照片品質要求嚴格，我們在有限的人力下，僅標註了700多張照片。值得注意的是，每張照片至少有7個字母需要進行標記。

如果對此資料集有其他疑問或想討論的，可聯繫: https://t.me/jtx257

Status Update

2023-11-28

• Although this project is named for its high precision, which is our aspiration, we currently lack the resources (time, manpower) to improve the accuracy of this project.
• If you require temporary high-precision recognition capabilities, please consider the first version training of this project, "robo-train-v1," which may offer a better.