License Plate Detection Using Convolutional Neural Network–Back to the Basic With Design of Experiments

Abstract

Automatic License Plate Recognition (ALPR) is one of the applications that hugely benefited from Convolutional Neural Network (CNN) processing which has become the mainstream processing method for complex data. Many ALPR research proposed new CNN model designs and post-processing methods with various levels of performances in ALPR. However, good performing models such as YOLOv3 and SSD in more general object detection and recognition tasks could be effectively transferred to the license plate detection application with a small effort in model tuning. This paper focuses on the design of experiment (DOE) of training parameters in transferring YOLOv3 model design and optimising the training specifically for license plate detection tasks. The parameters are categorised to reduce the DOE run requirements while gaining insights on the YOLOv3 parameter interactions other than seeking optimized train settings. The result shows that the DOE effectively improve the YOLOv3 model to fit the vehicle license plate detection task.

Stochastic Computing Convolution Neural Network Architecture Reinvented For Highly Efficient Artificial Intelligence Workload on Field Programmable Gate Array

“…Another side development has been attempted to integrate SC CNN to the front end of the YOLOv3 (You Only Look Once) algorithm [ 46 ] for automated license plate recognition (ALPR) in extending the existing work from [ 47 ]. SC is proven attractive in power efficiency and speed, which suit the harsh edge computing requirements for ALPR applications.…”

A Bio-Inspired Retinal Model as a Prefiltering Step Applied to Letter and Number Recognition on Chilean Vehicle License Plates

“…Consequently, low-pass filters can be considered within the OPL, which comprises horizontal cells and photoreceptors [26], enabling their behavior to be modeled with relative simplicity. The model for photoreceptors F ph ( f s , f t ) is expressed by Equation ( 5), whereas the model for horizontal cells F h ( f s , f t ) is determined by Equation (6). Both functions depend on the spatial frequency f s and temporal frequency f t .…”

“…The authors of [5] propose the edge-guided sparse attention network (EGSANet) for license plate detection, which utilizes edge contours and solves the problem of real-time detection. They also adjust parameters of the YOLOv3 network [6] to improve accuracy on a Malaysian license plate dataset from 87.75% to 99%.…”