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License plate detection with machine learning without using number recognition

Ohzeki Kazuo, Geigis Max, Schneider Stefan

Annals of Computer Science and Information Systems

2019

Vol. 18

333–-340

In autonomous driving, detecting vehicles together with their parts, such as a license plate is important. Many methods with using deep learning detect the license plate based on number recognition. However, there is an idea that the method using deep learning is difficult to use for autonomous driving because of the complexity in realizing deterministic verification. Therefore, development of a method that does not use deep learning (DL) has become important again. Although the authors have made the world's best performance in 2018 for Caltech data with using DL, this concept has now turned to another research without using DL. The CT5L method is the latest type, that includes techniques of the continuity of vertical and horizontal black-and-white pixel values inside the plate, unique Hough transform, only vertical and horizontal lines are detected, the top five in the order of the number of votes to ensure good performance. In this paper, a method to determine the threshold value for binarizing input by machine learning is proposed, and good results are obtained. The detection rate is improved by about 20 points in percent as compared to the fixed case. It achieves the best performance among the conventional fixed threshold method, Otsu's method, and the conventional method of JavaANPR.

Recognition of unknown scale objects

Żorski W.

Biuletyn Wojskowej Akademii Technicznej

2008

Vol. 57, nr 4

197-207

This paper presents an application of the Hough Transform to the task of identifying objects of unknown scale, e.g. within a scene in a robot vision system. The presented method is based on the Hough Transform for irregular objects, with a parameter space defined by translation, rotation and scaling operations. The high efficiency of the technique allows for poorquality or highly complex images to be analysed. The technique may be used in robot vision systems, identification systems or for image analysis, directly on grey-level images.

Artykuł prezentuje zastosowanie transformaty Hougha do zadań identyfikacji obiektów o nieznanej skali, np. na scenie w systemie widzenia robota. Metoda wykorzystuje transformatę Hougha dla wzorców nieregularnych, z przestrzenią parametrów określoną przez operacje: translacji, obrotu i skalowania. Wysoka sprawność techniki pozwala na analizę obrazów o słabej jakości lub dużej złożoności. Technika może być przykładowo zastosowana w systemach widzenia robotów, do bezpośredniej analizy obrazów pozyskanych w poziomach szarości.