An Efficient Deep Learning based License Plate Recognition for Smart Cities

Abstract

Computer vision algorithm with the amalgamation of deep learning technologies has provided endless possible applications. Currently, with the high load of vehicle traffic it is very difficult to trace and capture vehicular information over traffic surveillance on roads, parking or for safety concerns. Here, we have done an exploration for such a use case where a deep learning model is trained to detect and recognize a license plate in a vehicle. In the proposed method an object detection model, EfficientDet-D0 has been trained with custom dataset for license plate detection and have used optical character recognition model, Tesseract. In the proposed method, we have used a novel license plate extraction algorithm which reduces false localization followed by character recognition in a pipeline manner. We have also explored model quantization method to compress the model at reduced precision for efficient edge-based deployment for an end-application. In the proposed work, we have dedicated our study for Indian vehicles and have evaluated the performance with standard datasets like CCPD, UFPR and have achieved 97.9% in license localization and 95.15% in end-to-end detection and recognition respectively. We have implemented on Raspberry Pi3 and NVIDIA Jetson Nano deviced with improved performances. Comparing with state-of-the-art we have achieved 2×, 3.8× and 2.5× in CPU, GPU and edge platform respectively.

Keywords

• Deep learning
• License plate detection (LPD)
• Optical character recognition (OCR)
• Smart surveillance
• Edge intelligence
• Model quantization