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.
Highlights
Automatic number plate recognition (ANPR) systems have become a very important tool in many surveilling applications over the past few decades
This paper proposes an efficient framework for improving the performance of an licence plate detection (LPD) system
WORK This study proposed a new preprocessing method to improve the LPD system performance for complicated vehicle images by a Gaussian filter and the enhancement cumulative histogram equalization (ECHE) with the contrast-limited adaptive histogram equalization (CLAHE) algorithm
Summary
Automatic number plate recognition (ANPR) systems have become a very important tool in many surveilling applications over the past few decades. They are often used as a surveillance technique to identify licence plates of vehicles and are very useful for security systems, highway road tolling systems, traffic sign systems, tracking, and parking management systems [1]–[5]. The existing systems often work under some standard conditions, such as low-high lighting, rain, and limited day-night lighting. A robust licence plate detection (LPD) system is desirable to effectively work under all sorts of difficult conditions, such as night, dusk, rain, fog or snow; with images that are blurred, rotated, low-high lighting, distorted, with complex backgrounds and different colors.
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