GÖRÜNTÜ İŞLEMEDE NESNE KOORDİNAT ÖZELLİKLERİNİ KULLANARAK BAKLİYAT SAYMA İŞLEMİNE BİR YAKLAŞIM

Year 2020, Volume: 8 , 28 - 37, 31.12.2020

Muhammet Üsame Öziç , Nihat Çankaya , Muciz Özcan , Bariş Gökçe

https://doi.org/10.36306/konjes.822353

Cited By: 1

Abstract

Nesne sayma, gıda, medikal, endüstri ve günlük yaşamda farklı görevler için kullanılan bir süreçtir. Bu çalışmada, nesne sayma işleminin gerçekleştirilebilmesi için görüntü işleme tabanlı sistemler incelenmiş ve uygulamalar yapılmıştır. Uygulamalar için nesnelerin bir silo üzerinden akarak serici üzerinden eğimli bir şekilde ilerleyebileceği bir deney düzeneği tasarlanmıştır. Nesnelerin akışını gözlemleyebilecek endüstriyel bir kamera ve lens sistemi kullanılmıştır. Nesnelerin akışını izleyebilmek için sadece arka aydınlatma kullanılmıştır. Nesnelerin akış hızı ve nesnelerin serici çıkışı akabileceği eğim açısı değiştirilebilmektedir. Endüstriyel kamerada fps, piksel frekansı, pozlama süresi, görüntünün çözünürlüğü, ilgilenen alan seçimi, renkli ve renksiz görüntü alımı, görüntüdeki piksellerin kaç bit ile örnekleneceği kullanıcı tarafından seçilebilmektedir. Algoritma tasarımı için Python yazılımı ve OpenCV kütüphanesi kullanılmıştır. Nesne sayımı için 100 adet nohut tanesi belirlenmiş ve belirli bir süre kameradan video kaydı elde edilmiştir. Video üzerinde arka plan çıkarma işlemi uygulanarak sadece nesnelerin beyaz renk olarak görülebileceği ikinci bir video elde edilmiştir. Binary formata dönüştürülen videoda nesne çevresi sınırlayıcı en küçük dikdörtgen koordinat değerleri ve nesne ağırlık merkezi koordinat değerleri elde edilmiştir. Video görüntüsü üzerinde sabit tek ve çift sanal çizgiler çekilerek sayma işleminin gerçekleştirileceği metotlar geliştirilmiştir. Koordinat değerlerinin ve sanal çizgilerin sayma işlemi için kullanılmasında ortaya çıkan avantaj ve dezavantajlar bu çalışma sonucunda tartışılmıştır.

Keywords

Nesne Sayma, Görüntü İşleme, Kamera, Python, OpenCV

Supporting Institution

NECMETTİN ERBAKAN ÜNİVERSİTESİ BİLİMSEL ARAŞTIRMA KOORDİNATÖRLÜĞÜ

Project Number

181719004

An Approach to Counting Legumes Using Coordinate Features in Image Processing

Abstract

: Object counting is a process used for different tasks in food, medical, industry and daily life. In this study, image processing-based systems have been examined and applications have been made to perform the object counting process. For the applications, an experimental setup has been designed in which the objects can flow over a silo and proceed in an inclined manner over the spreader. An industrial camera and lens system that can observe the flow of objects were used. Only backlighting was used to monitor the flow of objects. The flow rate of objects and the angle of inclination through which objects can flow out of the spreader can be changed. In the industrial camera, fps, pixel frequency, exposure time, image resolution, area of interest selection, color and mono image acquisition, how many bits the pixels in the image will be sampled can be selected by the user. Python software and OpenCV library were used for algorithm design. For the object counting, 100 chickpea seeds were determined and the video was recorded from the camera for a certain period of time. By applying a background subtraction process on the video, a second video was obtained in which only the objects can be seen as white. The smallest rectangular coordinate values bounding the object and the object centroid coordinate values were obtained in the video converted into binary format. Methods have been developed for counting by drawing single and double virtual lines in the video. The advantages and disadvantages of using coordinate values and virtual lines for counting have been discussed as a result of this study.

Keywords

Object Counting, Image Processing, Camera, Python, OpenCV

Project Number

181719004

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