Towards Real-Time LPD on Resource-constrained CPUs: An Empirical Evaluation of Pruning and Quantization for YOLOv8

Abstract

The growing incorporation of deep learning into autonomous robotic systems has highlighted the importance of efficient inference on resource-constrained edge platforms. Moreover, License Plate Detection (LPD) is a key component of intelligent transportation systems, requiring high accuracy and real-time performance for practical deployment. However, deep learning detectors often remain computationally demanding for edge devices with limited resources. This paper proposes an edge-oriented optimization framework for YOLOv8-based LPD, combining structured pruning, INT8 quantization. Extensive experiments evaluate the trade-off between accuracy, computational complexity, and inference latency. Results show that combining pruning and quantization significantly reduce model size and computational cost while preserving detection performance. The optimized model achieves real-time inference exceeding 100 FPS on an Intel CPU, demonstrating its suitability for resource-constrained edge environments. These findings confirm that hybrid compression strategies enable efficient deployment of deep learning–based LPD systems in real-world applications.