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Paper Title

AN EDGE–CLOUD DEEP-LEARNING FRAMEWORK FOR SUSTAINABLE VEHICLE-COMPLIANCE MONITORING USING YOLOV8 AND CNN-BASED HSRP AUTHENTICATION

Article Identifiers

Registration ID: IJNRD_327340

Published ID: IJNRD2607322

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Keywords

sustainable smart cities; intelligent transportation systems; YOLOv8; HSRP authentication; edge–cloud computing; vehicle-compliance monitoring; SDG 11.

Abstract

Rapid motorization is straining the capacity of manual road-side enforcement, and non-compliant vehicles those carrying counterfeit plates, lapsed insurance, or operating well beyond their fitness life impose disproportionate safety and environmental costs on growing cities. Most reported number-plate systems stop at reading characters and rarely connect recognition to the wider question of whether a vehicle is legally and environmentally fit to be on the road. This paper presents an edge–cloud framework that couples a YOLOv8 detector with a purpose-built convolutional neural network (CNN) so that optical character recognition (OCR) and physical High-Security Registration Plate (HSRP) authentication run as two concurrent streams, after which a configurable rule engine screens insurance validity and vehicle age against a policy-driven threshold. The models were developed and evaluated on a custom Indian HSRP dataset (6,000 plate images spanning day, night, rain, motion-blur and low-resolution conditions, split 70/15/15 on a vehicle-disjoint basis). On the held-out test set the YOLOv8 plate detector reached mAP@0.5 = 98.1% and mAP@0.5:0.95 = 74.3%; the HSRP classifier reached 96.75% accuracy, 97.27% precision, 96.20% recall and an F1-score of 96.73%; OCR reached 95.84%-character accuracy and 92.7% exact-plate accuracy; and the complete pipeline delivered an end-to-end latency of about 33 ms per frame (≈30 FPS) on an NVIDIA RTX 3060. A sustainability assessment expresses the framework’s potential contribution to SDG 11 and SDG 13 through an emissions-avoidance model driven by the number of flagged overage vehicles. The results indicate that combining edge-based plate analytics with configurable regulatory screening is a workable route to sustainable smart-city transport management; full deployment, however, requires authorized database access and jurisdiction-specific policy rules.

How To Cite (APA)

Sonal S. Kumbhar & Naresh A. Kamble (July-2026). AN EDGE–CLOUD DEEP-LEARNING FRAMEWORK FOR SUSTAINABLE VEHICLE-COMPLIANCE MONITORING USING YOLOV8 AND CNN-BASED HSRP AUTHENTICATION. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 11(7), d197-d210. https://ijnrd.org/papers/IJNRD2607322.pdf

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Other Publication Details

Paper Reg. ID: IJNRD_327340

Published Paper Id: IJNRD2607322

Research Area: Other area not in list

Author Type: Indian Author

Country: Ichalkaranji, Maharashtra, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2607322.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2607322

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Publication Timeline

Paper Submission
17-07-2026
Peer Review
Through Scholar9.com Platform
Paper Acceptance
23-07-2026
Paper Publication
25-07-2026

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