Accurate Detection and Recognition of Dirty Vehicle Plate Numbers for High-Speed Applications

ABSTRACT:

This paper presents an online highly accurate system for automatic number plate recognition (ANPR) that can be used as a basis for many real-world ITS applications. The system is designed to deal with unclear vehicle plates, variations in weather and lighting conditions, different traffic situations, and high-speed vehicles. This paper addresses various issues by presenting proper hardware platforms along with real-time, robust, and innovative algorithms. We have collected huge and highly inclusive data sets of Persian license plates for evaluations, comparisons, and improvement of various involved algorithms. The data sets include images that were captured from crossroads, streets, and highways, in day and night, various weather conditions, and different plate clarities. Over these data sets, our system achieves 98.7%, 99.2%, and 97.6% accuracies for plate detection, character segmentation, and plate recognition, respectively. The false alarm rate in plate detection is less than 0.5%. The overall accuracy on the dirty plates portion of our data sets is 91.4%. Our ANPR system has been installed in several locations and has been tested extensively for more than a year. The proposed algorithms for each part of the system are highly robust to lighting changes, size variations, plate clarity, and plate skewness. The system is also independent of the number of plates in captured images. This system has been also tested on three other Iranian data sets and has achieved 100% accuracy in both detection and recognition parts. To show that our ANPR is not language dependent, we have tested our system on available English plates data set and achieved 97% overall accuracy.

PROJECT OUTPUT VIDEO:

EXISTING SYSTEM & DISADVANTAGES:

• For character segmentation, there are many algorithms based on morphological operations, and connected component analysis (CCA).

• In such methods, it is necessary to apply a proper thresholding method to obtain a binary image of the plate before any further processing. For example, CCA, which has been used in many research works, depends highly on the previously applied thresholding method.

• Thresholding methods like Niblack, SAUVOLA, Wolf and Jolion and OTSU are good candidates for plate binarization. Applying such algorithms on plate candidates relies on appropriate setting of the involved parameters.

• Unfortunately, at the detection step there is no information about the input plate quality and the parameters cannot be tuned appropriately. Therefore, the recognition accuracy of such algorithms decreases when different plate qualities are involved.

PROPOSED SYSTEM:

• We must say that many ITS applications depend on real-world Automatic Number Plate Recognition (ANPR) systems.

• The performance of ANPR systems are degraded by low clarity of the vehicles plates, variations in weather and lighting conditions, and high vehicle speeds.

• For instance, most of well-known plate detection and recognition algorithms fail on dirty plates.

• This paper presents a system that overcomes such issues by presenting proper hardware platforms along with real-time, robust, and innovative algorithms.

• The system can deal with, various weather and environmental conditions, different plate formats and languages, low plates clarities, high vehicle speeds and various traffic situations in online real-world applications.

ADVANTAGES OF PROPOSED SYSTEM:

• The main advantage of our system is its high detection and recognition accuracies on dirty plates.

• To achieve reliable evaluations, two new data sets were created and used in this paper: one for violation detection called “Crossroad Data set” and the other for vehicle counting in highways called “Highway Data set.” The accuracies of our system on the Crossroad Data set are 98.7%, 99.2%, and 97.6% for plate detection, character segmentation, and plate recognition parts, respectively. In vehicle counting application, the detection rate and false alarm rate over the Highway data set are 99.1% and 0.5%, respectively.

• We have tested this system on a publicly available English plate data set as well and achieved an overall accuracy of 97%. The proposed system is compared to many reported ANPR systems from different point of views. By considering the practical aspects, several copies of our ANPR system have been installed in different intersections and highways of Tehran, capital city of Iran. These systems have been tested day and night over a year and presented robust and reliable performances, in different weather conditions, such as rainy, snowy, and dusty.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS:

• System           : Pentium Dual Core.

• Hard Disk      : 120 GB.

• Monitor          : 15’’ LED

• Input Devices : Keyboard, Mouse

• Ram                : 1GB.

SOFTWARE REQUIREMENTS:

• Operating system : Windows 7.

• Coding Language : MATLAB

• Tool                      : MATLAB R2013A

REFERENCE:

Rahim Panahi, Member, IEEE, and Iman Gholampour, Member, IEEE, “Accurate Detection and Recognition of Dirty Vehicle Plate Numbers for High-Speed Applications”, IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2017