<![CDATA[Automatic Number Plate Recognition (ANPR) is crucial for intelligent transportation systems but often falters in real-world conditions due to environmental variations. This study constructed a robust and computationally efficient vehicle license plate detection system that achieved high accuracy under diverse real-world challenges and was deployable on resource-constrained edge hardware for real-time operation. The proposed holistic framework integrated three key components: (a) the creation of the Dynamic Vehicle License Plate Dataset (DVLPD) v1.0, containing 866 annotated images with variations in lighting, weather, and camera angles; (b) the implementation of a targeted data augmentation pipeline employing geometric and photometric transformations to enhance model robustness; and (3) the architectural optimization of a You Only Look Once (YOLO) version 8 or YOLOv8 model through pruning, quantization, and hyperparameter tuning specifically for edge deployment. The optimized model achieved a mean average precision (mAP) of 91% on the test set. When deployed on a Raspberry Pi 4 in a prototype parking system, it demonstrated practical viability with an inference latency of 0.4 seconds per frame and an error rate of 4.2%. The results validate that the integration of a diverse dataset, strategic augmentation, and model optimization can yield an accurate and efficient ANPR solution suitable for real-time edge applications. Future work will focus on expanding the dataset to include more extreme conditions for greater generalization.]]>
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