Developing Learning-Based Preprocessing Methods for Detecting Complicated Vehicle Licence Plates

Abstract

A licence plate detection (LPD) system is an important tool in several roadway traffic applications. This study aims to develop an advanced detection system that works well in complicated scenarios. It proposes a robust preprocessing enhancement method for accurately detecting the licence plates from difficult vehicle images. The proposed method includes the combination of a Gaussian filter, an enhancement cumulative histogram equalization method, and a contrast-limited adaptive histogram equalization technique. The local binary pattern and median filter with histogram of oriented gradient descriptors are used as powerful tools to extract key features from three types of licence plate resolutions. The extracted features are used as input to support vector machine classifier. Processing methods, such as a position-based method are used with the detector to reduce unwanted bounding boxes, as well as false positive values. Four databases consisting of 2050 vehicle images under different conditions are used. Various detection metrics, object localization, and the receiver operating characteristic (ROC) curve are used to evaluate the performance of the proposed method. The experimental results on vehicles databases in several languages, including English, Chinese, and Arabic number plates, show that the proposed method has achieved significant performance improvements. It outperforms the state-of-the-art approaches in terms of both the detection rate and the processing time. The detection rate when trained with 1520 LP images is 99.62% with a false positive rate of 1.675% for complicated images. The average detection time per vehicle image is 0.2408 milliseconds.

Automatic Number Plate Recognition:A Detailed Survey of Relevant Algorithms

“…SVM is used for character recognition in [ 126 , 127 ]. For challenging and complicated number plates, an improved SVM based algorithm is proposed in [ 128 ]. In [ 109 , 129 ], the features vector is produced by vertical and horizontal binary character projection.…”

Computer Vision Applications in Intelligent Transportation Systems: A Survey

“…Support vector regression and Gaussian process regression models are used in the literature for regression. Supervised learning models have been used for the classification of vehicles [ 11 , 12 ], classification of traffic lights [ 13 ], recognition and classification of license plate characters [ 14 , 15 , 16 , 17 ], detection of traffic signs [ 18 ], detection of pedestrians [ 19 , 20 ], etc.…”

“…An improved SVM-based algorithm was proposed in [ 14 ] for challenging and complex plates, and a self-learning algorithm based on Bayesian probability and Levenshtein text-mining, which can improve the matching accuracy of the ANPR system, was proposed in [ 124 ].…”

A Review of Image Processing Techniques for Pest and Disease Early Identification Systems on Modern Cocoa Plantation

“…Model Kombinasi Butterworth low-pass filtering, Butterworth high-pass enhanced filtering, and adaptive weighted median filtering [47], Combination of Median Filter HOG (MHOG) and LBP [48]. [35] PC4 (Classification Approach) one-class support vector machines (OC-SVM) [49], Hybrid model Empirical wavelet transform (EWT) dan SVM [49], Hybrid model dynamic reliability measure (DReM) and support vector machines (SVMs) [50], Convolutional Neural Network Model [51].…”