Open Access
ARTICLE
Mitigating Fuel Station Drive-Offs Using AI: YOLOv8 OCR and MOT History API for Detecting Fake and Altered Plates
Milinda Priyankara Bandara Gamawelagedara1, Mian Usman Sattar1, Raza Hasan2,*
1 College of Science and Engineering, University of Derby, Derby, DE22 1GB, UK
2 Department of Science and Engineering, Southampton Solent University, Southampton, SO14 0YN, UK
* Corresponding Author: Raza Hasan. Email: 
(This article belongs to the Special Issue: Artificial Intelligence Algorithms and Applications)
Computers, Materials & Continua https://doi.org/10.32604/cmc.2025.062826
Received 28 December 2024; Accepted 24 March 2025; Published online 21 April 2025
Abstract
Fuel station drive-offs, wherein the drivers simply drive off without paying, are a major issue in the UK (United Kingdom) due to rising fuel costs and financial hardships. The phenomenon has increased greatly over the last few years, with reports indicating a substantial increase in such events in the major cities. Traditional prevention measures such as Avutec and Driveoffalert rely primarily on expensive infrastructure and blacklisted databases. Such systems typically involve costly camera installation and maintenance and are consequently out of the budget of small fuel stations. These conventional approaches also fall short regarding real-time recognition, particularly regarding first-time impostors using fictitious plates, which represent an increasingly significant proportion of such forgery. This research presents an AI (Artificial Intelligence)-driven detection system using the MOT (Ministry of Transport) History API (Application Programming Interface) to scan in real-time at gas stations to recognize and prevent such fraud. The system integrates various state-of-the-art technologies to offer a foolproof system. Using the latest YOLO (You Only Look Once) model to recognize number plates and EasyOCR (Optical Character Recognition) to recognize characters, the system correctly reads license plates in various environmental conditions like lighting, viewpoint, and weather conditions. This approach minimizes the utilization of expensive camera systems and employs cheaper ANPR (Automatic Number Plate Recognition) gear, availing existing installed surveillance cameras on filling stations. The system operates with a basic web-based application to notify operators of stolen vehicles in real-time, enabling them to react immediately. Real-world testing achieves 84% success with CCTV (Closed-Circuit Television) images, depicting its real-world applicability. The results indicate that the AI-driven solution offers a monumental leap compared to current practices, giving fuel stations a cost-effective and efficient means of reducing financial loss from drive-off incidents.
Keywords
EasyOCR; MOT; ANPR; YOLO model; CNNs
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