IÇ ORTAMLARDA KAPILARIN TESPİTİ İÇİN DERİN ÖĞRENME TEKNİKLERİNİN KARŞILAŞTIRILMASI

Year 2021, Volume: 29 Issue: 3, 396 - 412, 31.12.2021

Burak Kaleci Kaya Turgut

https://doi.org/10.31796/ogummf.889095

Abstract

İç ortamlarda kapıların (açık, yarı açık ve kapalı) tespit edilmesi robotik, bilgisayarlı görü ve mimari gibi çok çeşitli uygulama alanlarında kritik bir görevdir. Kapı tespiti problemine çözüm bulmaya çalışan çalışmalar üç temel kategoriye ayrılabilir: 1) görsel veri ile kapalı kapılar, 2) mesafe verisi ile açık kapılar ve 3) nokta bulutu verisi ile açık, yarı açık ve kapalı kapılar. Kapıları görsel ve mesafe verisi ile bazı belirli şartlar altında başarılı bir şekilde bulan yöntemler önerilmiş olsa da bu çalışmada sahnelerin 3B karakteristiğini anlatma kabiliyeti sebebiyle nokta bulutu verisi kullanılmıştır. Bu çalışmanın iki temel katkısı bulunmaktadır. Birincisi, kapının tipi ve verinin karakteristiğine bağlı olarak genellikle bir kurallar kümesi tanımlayan önceki çalışmalardan farklı olarak PointNet, PointNet++, Dinamik Çizge Erişimsel Sinir Ağları (DGCNN), PointCNN ve Point2Sequence gibi nokta tabanlı derin öğrenme mimarilerinin potansiyelinin keşfedilmesini amaçlanmıştır. İkincisi, GAZEBO benzetim ortamında farklı robot konum ve yönelimleriden elde edilen nokta bulutlarından oluşan OGUROB DOORS veri kümesi oluşturulmuştur. Bu mimarilerin olumlu ve olumsuz yönlerini analiz etmek için kesinlik, duyarlılık ve F1 skor ölçütlerini kullandık. Buna ek olarak, mimarilerin karakteristiklerini ortaya koymak amacıyla bazı görsel sonuçlar verilmiştir. Test sonuçları bütün mimarilerin açık, yarı açık ve kapalı kapıları %98 üzerinde bir başarı ile sınıflandırabildiğini göstermiştir.

Keywords

Kapı Bulma, Nokta Bulutu Verisi, Derin Öğrenme, Kapı Veri Kümesi

COMPARISON OF DEEP LEARNING TECHNIQUES FOR DETECTION OF DOORS IN INDOOR ENVIRONMENTS

Abstract

In indoor environments, the detection of doors (open, semi-opened, and closed) is a crucial task for a variety of fields such as robotics, computer vision, and architecture. The studies that are addressed the door detection problem can be divided into three major categories: 1) closed doors via visual data, 2) open doors via range data, and 3) open, semi-opened, and closed doors via point cloud data. Although some successful studies have been proposed being detected doors via visual and range data under specific circumstances, in this study, we exploited point cloud data due to its ability to describe the 3D characteristic of scenes. The main contribution of this study is two-fold. Firstly, we mainly intended to discover the potential of point-based deep learning architectures such as PointNet, PointNet++, Dynamic Graph Convolutional Neural Network (DGCNN), PointCNN, and Point2Sequence, in contrast to previous studies that generally defined a set of rules depending on the type of door and characteristics of the data, Secondly, the OGUROB DOORS dataset is constructed, which contains point cloud data captured in the GAZEBO simulation environment in different robot positions and orientations. We used precision, recall, and F1-score metrics to analyze the merit and demerit aspects of these architectures. Also, some visual results were given to describe the characteristics of these architectures. The test results showed that all architectures are capable of classifying open, semi-opened, and closed doors over 98% accuracy.

Keywords

Door Detection, Point Cloud Data, Deep Learning, Door Dataset

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