Sulama/İlaçlama Robotu için Nesne Tanıma Çalışmaları

Year 2021, Issue: Ejosat Ek Özel Sayı (HORA), 25 - 33, 28.02.2021

Haluk Özgen , Metin Turan

https://doi.org/10.31590/ejosat.1115830

Abstract

Yapay zekâ ve buna bağlı alt çalışma konularındaki bilimsel yöntemler, neredeyse her alanda yaygın biçimde kullanılmaktadır. Özellikle savunma sanayi ve tarım bu alanların en cazip uygulama merkezlerini oluşturmaktadırlar. Tarımda veya bahçelerde kullanılmak üzere daha önce geliştirmiş olduğumuz sulama ve ilaçlama prototip mobil robotumuzun, nesne (ağaç veya değil) tanıma kısmında yeterli düzeyde başarılı neticeler alınamamıştı. Bu çalışmada, bu mobil robot ile gerçek zamanlı görüntüler üzerinde karmaşık (asimetrik) yapıya sahip bitki nesnelerinin tanınması ve bu tanınma sonucuna göre ilaçlama mekanizmasının tetiklenmesinin başarımını artırılmıştır. Bitki nesnelerinin tanınması amacı ile Viola-Jones nesne tanıma algoritması kullanılmıştır. Bu algoritma ile yapılan eğitim ve parametrik düzenlemeler sonucunda elde edilen modelin 5 farklı senaryo yolu üzerinde sınanması sonucunda, mobil sulama/ilaçlama robotunun görme yetisi ve bitki nesnelerini tanıma başarısının yaklaşık olarak %96.5 gibi oldukça yüksek bir düzeye çıkarıldığı görülmüştür. Ayrıca, ihtiyaç dolayısı ile uygulanan, herhangi bir nesneye ait görüntü eğitim setinin oluşturulmasına yönelik hızlı ve kolay bir yöntemin detayları makalede açıklanmıştır.

Keywords

Mobil robotlar, nesne tanıma, görüntü seti oluşturma, yapay zeka, akıllı sistemler

Object Recognition Studies for Irrigation/Medication Robot

Abstract

Scientific methods in artificial intelligence and related sub-studies are widely used in almost every field. Defense industry and agriculture are the most attractive application areas of these fields. Sufficient successful results could not be obtained in the object (tree or not) recognition part of our irrigation and spraying prototype mobile robot, which we developed earlier for use in agriculture or gardens. In this study, with this mobile robot, the performance of the identification of complex (asymmetric) plant objects on real-time images and the triggering of the spraying mechanism according to this recognition result has been increased. Viola-Jones object capture algorithm was used to identify plant objects. As a result of the testing the model obtained through the training and parametric arrangements made to this algorithm on 5 different scenario paths, it has been observed that the success of recognizing plant objects and vision ability of the mobile irrigation/medication robot has been increased to a very high level of approximately 96,5%. In addition, the details of a quick and easy method for creating an image training set for any object that is applied due to need are explained in the article.

Keywords

Mobile robots, object detection, creating image set, artifical intelligence, smart systems

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