AI-Enhanced Camera Systems for Real-Time Identification of Expired Vehicle Pollution and Insurance via License Plate Recognition

In the recent past, developments in intelligent transportation systems have called for the need to implement automatic scanning solutions. Such scanning solutions were required to scan vehicles against environmental and safety standards. The following paper discusses the application of artificial intelligence in camera systems to automatically scan for expired pollution certificates and insurance using number plate recognition capability. This vehicle registration number captures in real time and crosschecks it with the centralized database through advanced image processing and machine learning algorithms. With this, authorities can immediately check and verify if pollution control and insurance certificates are valid; thus, it reduces manual inspections and provides improved efficiency. The proposed system uses CNN-based high-accuracy license plate detection and OCR for obtaining the extracted registration number. That number is then passed over an AI model to identify vehicles whose certifications have expired and flags them for further action. Experimental results demonstrate that the system will reliably identify, albeit quickly, non-compliant vehicles with minimal error, thus making it a viable solution in an urban and highway setting. This technology presents much promise for regulatory bodies looking to enforce compliance of vehicles for safer, ecologically friendly roads.

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Publication Details

Journal
International Research Journal on Advanced Engineering and Management (IRJAEM)
Published
2024-12-12
DOI
https://doi.org/10.47392/irjaem.2024.0528
Primary Topic
Vehicle License Plate Recognition
Type
article
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article

AI-Enhanced Camera Systems for Real-Time Identification of Expired Vehicle Pollution and Insurance via License Plate Recognition

S Binny, Kumar Rahul, K. S. Jishnu, Kevin Gómez et al.
International Research Journal on Advanced Engineering and Management (IRJAEM)
Vehicle License Plate Recognition
article

AI-Enhanced Camera Systems for Real-Time Identification of Expired Vehicle Pollution and Insurance via License Plate Recognition

S Binny, Kumar Rahul, K. S. Jishnu, Kevin Gómez, A. Nair, C B Abhinand
article en

Abstract

In the recent past, developments in intelligent transportation systems have called for the need to implement automatic scanning solutions. Such scanning solutions were required to scan vehicles against environmental and safety standards. The following paper discusses the application of artificial intelligence in camera systems to automatically scan for expired pollution certificates and insurance using number plate recognition capability. This vehicle registration number captures in real time and crosschecks it with the centralized database through advanced image processing and machine learning algorithms. With this, authorities can immediately check and verify if pollution control and insurance certificates are valid; thus, it reduces manual inspections and provides improved efficiency. The proposed system uses CNN-based high-accuracy license plate detection and OCR for obtaining the extracted registration number. That number is then passed over an AI model to identify vehicles whose certifications have expired and flags them for further action. Experimental results demonstrate that the system will reliably identify, albeit quickly, non-compliant vehicles with minimal error, thus making it a viable solution in an urban and highway setting. This technology presents much promise for regulatory bodies looking to enforce compliance of vehicles for safer, ecologically friendly roads.

International Research Journal on Advanced Engineering and Management (IRJAEM)Vol. 2(12)
Software Training and Development Centre (IN)
Openalex Percentile: Top 65%
Vehicle License Plate Recognition
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AI-Enhanced Camera Systems for Real-Time Identification of Expired Vehicle Pollution and Insurance via License Plate Recognition — S Binny, Kumar Rahul, et al. · International Research Journal on Advanced Engineering and Management (IRJAEM) (2024) | TGRS Research Map | TGRS