An Enhanced Method for Detecting the Shaded Images of the Car License Plates based on Histogram Equalization and Probabilities

Abstract

Shadow is one of the major and significant challenges in detection algorithms which track the objects such as the license plates. The quality of images captured by cameras is influenced by weather conditions, low ambient light and low resolution of the camera. The shadow in images reduces the reliability of the sight algorithms of the device as well as the visual quality of images. The previous papers indicate that no effective method has been presented to improve the license plate detection accuracy of the shaded images. In other words, the methods that have been presented for automatic license plate detection in shadowed images until now use a combination of color features and texture of the image. In all these methods, in order to detect the frame of the shadow and the texture of the image, sufficient light is required in the image; this necessity cannot be found in most of the regular images captured by road cameras. In order to solve this problem, an improved license plate detection method is presented in this research which is able to detect the license plate area in shadowed images effectively. In fact, this is a contrast-improving method which utilizes the dual binary method for automatic plate detection and is introduced to analyze the interior images with low contrast, and also night shots, blurred and shadowed images. In this method, the histogram of the image is firstly calculated for each dimension and then the probability of each pixel in the whole image is obtained. As a result, after calculating the cumulative distribution of the pixels and replacing it in the image, it will be possible to remove the shadow from the image easily. This new method of detection was tested and simulated for 1000 images of vehicles under different conditions. The results indicated the detection accuracy of 90/30, 97/87 and 98/70 percent for the license plates detection in three databases of University of Zagreb, Numberplates.com and National Technical University of Athens, respectively. In other words, comparing the performance of the proposed method with two similar and new methods, namely Hommos and Azam, indicates an average improvement of 26/70 and 72/95 percent for the plate detection and 32/38 and 36/53 percent for the time required for rapid and correct license plate detection, even in shaded images.

Authors and Affiliations

Mohammad Faghedi, Behrang Barekatain, Kaamran Raahemifar

Keywords

Related Articles

Collaborative Editing over Opportunistic Networks: State of the Art and Challenges

Emerging Opportunistic Networks (ON) are under intensive research and development by many academics. However, research efforts on ON only addressed routing protocols as well as data dissemination. Too little attention wa...

 Solving Semantic Problem of Phrases in NLP Using Universal Networking Language (UNL)

 This paper largely deals with the Semantic problem and generation of semantic relations, which are difficult problems in the field of natural language processing. In this work we looked at it through the knowledge...

BYOD Implementation Factors in Schools: A Case Study in Malaysia

The Bring Your Own Device (BYOD) initiative has been implemented widely in developed countries as a mechanism to prepare the students for the 4th industrial revolution. Success stories of the initiative vary depending on...

Intelligent Wireless Indoor Monitoring System based on ARM

This paper proposed an intelligent wireless indoor monitoring system based on STM32F103. The system compromises a master and terminals, which communicates through a CC1101 433M wireless unit. Using ENC28J60 and SIM900A t...

Modifications of Particle Swarm Optimization Techniques and Its Application on Stock Market: A Survey

Particle Swarm Optimization (PSO) has become popular choice for solving complex and intricate problems which are otherwise difficult to solve by traditional methods. The usage of the Particle Swarm Optimization technique...

Download PDF file
  • EP ID EP408218
  • DOI 10.14569/IJACSA.2018.091056
  • Views 91
  • Downloads 0

How To Cite

Mohammad Faghedi, Behrang Barekatain, Kaamran Raahemifar (2018). An Enhanced Method for Detecting the Shaded Images of the Car License Plates based on Histogram Equalization and Probabilities. International Journal of Advanced Computer Science & Applications, 9(10), 456-466. https://europub.co.uk/articles/-A-408218