Smart car plate recognition system using multi-task learning - Khor, Yin Loon (2024)
Automatic License Plate Recognition (ALPR) systems are crucial in extracting vehicle information. However, ALPR alone is insufficient for robust vehicle owner identification, especially in the event of misidentification or covered license plates (LPs). Acknowledging the significance of vehicle colour in enhancing identification accuracy, this project proposes a more secure and comprehensive approach by integrating Vehicle Colour Recognition (VCR) with LP detection and Optical Character Recognition (OCR) tasks. Unlike the conventional two-stage ALPR systems, this solution introduces a novel onestage YOLO-based multi-task model. It incorporates additional object detection heads onto the YOLO backbone, allowing for parallel processing and efficient real-time detection for all three tasks. The proposed model achieves spectacular results with mean Average Precision (mAP) scores of 0.778, 0.963, and 0.881 for OCR, LP detection, and VCR, respectively. Promisingly, this model is comparable to single-head, single-task models, which are trained solely for each task. It outperforms a single-head multi-task model, which naively shares all tasks using one single head. Specifically, the model is 1.77x faster than the conventional approach, which involves inference of single-task models for OCR, LP, and VCR sequentially. Experimental results demonstrate that the proposed solution is robust in simultaneously addressing OCR, LP detection, and VCR within a unified, single-stage framework.