ORB yöntemi ile oy tespiti ve sayımını gerçekleştiren sistemin tasarımı

Abstract

Bu çalışmada, oy tespitini ve sayımını gerçekleştiren görüntü işleme tabanlı bir sistem geliştirilmiştir. Sistem, donanımsal ve yazılımsal olmak üzere iki bölümden oluşmaktadır. Görüntü almak için kullanılan kamera ve görüntü işlemek için kullanılan Raspberry Pi3 donanım kısmını oluşturmaktadır. Yazılım kısmında ise görüntü işleme yöntemlerinden olan Oriented FAST and Rotated BRIEF (ORB) metodu ile Brute-Force Eşleştirmesi kullanılarak görüntülerdeki öznitelikler eşleştirilmektedir. Yazılımın kodları Python programlama dilinde yazılmış olup ve OpenCV kütüphanesinden faydalanılmıştır. Çalışmada oy tespiti ve sayımı için 72 punto büyüklüğünde, Calibri yazı tipi ile yazılmış EVET ve HAYIR oy pusulaları kullanılmıştır. Sistemde yüksek çözünürlüklü kamera sayesinde oy pusulasının görüntüsü alınmakta ve görüntü işleme yazılımına aktarılmaktadır. Yazılımın çalışma mantığı, kayıtlı görüntüdeki köşe ve dönüm noktaları gibi ayırt edici özelliklerin belirlenmesi ve belirlenen özelliklerin, kamera tarafından çekilen görüntüler ile eşleştirilmesi prensibine dayanmaktadır. Oyun kime verildiğinin tespiti için ise kullanılan oyun yatay düzlemindeki konumuna bakılmaktadır. Sistem eşleşen görüntüye göre evet ve hayır oy sayılarını arttırmaktadır. Yapılan çalışmada %100 başarı oranı ile oy tespiti ve sayımı gerçekleşmiştir.

Keywords

• Oy tespiti ve sayımı
• Görüntü işleme
• ORB
• Brute-Force Eşleştirme

Design of the system that performs vote detection and counting by ORB method

Abstract

In this study, an image processing-based system was developed that performs vote detection and counting. The system consists of two parts, hardware and software. The camera used for image capturing and Raspberry Pi3 used for image processing constitute the hardware part. In the software part of the system, the features in the images are matched using Oriented FAST and Rotated BRIEF (ORB) method and Brute-Force Matching method which are the image processing methods. The software's codes are written in the Python programming language and the OpenCV library is used. In the study, YES and NO ballots written in Calibri font, 72 points in size, were used for vote detection and counting. Thanks to the high-resolution camera in the system, the image of the ballot is taken and transferred to the image processing software. The operating logic of the software works according to the principle of determining the corners and milestones, which are distinctive features in the recorded image, and matching them with the images taken. In order to determine who the game was given to, the position of the game on the horizontal plane used is looked at. The system increases the number of yes and no votes according to the matching image. In the study, it is performed that vote determination and counting with a 100% success rate.

Keywords

• Ballot detection and counting
• Image processing
• ORB
• Brute-Force Matching

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