An Efficient Deep Learning based License Plate Recognition for Smart Cities

Abstract

With the high load of vehicle traffic, tracing and capturing vehicular information over traffic surveillance on roads, parking, or for safety concerns is tough. In the proposed method, a deep learning-based object detection model, EfficientDet-D0, has been trained with the custom dataset for license plate detection and used an optical character recognition model, Tesseract. In the proposed method, we have used an improved license plate extraction algorithm, which reduces false localization followed by character recognition in a pipeline manner. We have also explored the model quantization method to compress the model with reduced precision for efficient edge-based deployment for an end application. In the proposed work, we have dedicated our study to Indian vehicles, evaluated the performance with standard datasets like CCPD and UFPR, and have achieved 97.9% in license localization and 95.15% in end-to-end detection and recognition, respectively. We have implemented it on Raspberry Pi3 and NVIDIA Jetson Nano devices with improved performances. Compared with state-of-the-art, we have achieved 2×, 3.8×, and 2.5× in CPU, GPU, and edge platform, respectively.