From YOLO Models to Embedded Deployment: A Blockchain-Enabled ANPR System on Jetson and Raspberry Pi

Abstract

This paper presents a secure blockchain-enabled Automatic Number Plate Recognition (ANPR) framework for Intelligent Transportation Systems (ITS), combining lightweight YOLO-based license plate detection, OCR-based character recognition, and Ethereum smart contracts for tamper-resistant vehicle data management. The study investigates the inference performance of recent object detection architectures, namely YOLOv11, YOLOv12, and YOLO26, on Raspberry Pi 5 and NVIDIA Jetson Nano edge devices. Furthermore, optimizations for ONNX and TensorRT are explored to enhance inference efficiency on embedded hardware. Experimental results demonstrate complementary strengths among the evaluated models. YOLOv11n achieved the best overall detection performance, obtaining the highest recall (0.945), F1-score (0.9645), and mAP@0.5:0.95 (0.692), while YOLOv11n and YOLOv12n achieved identical precision (0.985) and mAP@0.5 (0.963). YOLO26 demonstrates the best deployment efficiency, reducing inference latency to 33.4 ms on Raspberry Pi (ONNX) and 38.9 ms on Jetson Nano, making it suitable for edge applications. Model optimization significantly accelerates inference, reducing YOLOv11 latency on Raspberry Pi from 98.2 ms to 38.3 ms after ONNX conversion. The results demonstrate that the proposed framework effectively balances detection accuracy, computational efficiency, and data security, making it a promising solution for smart parking and next-generation ITS applications.