Deep Learning-based Salient Object Detection in 2D Images

Abstract

Saliency detection is the process of determining how different or attention-grabbing a region within an image is compared to others. In this study, deep learning methods and image processing techniques were used for salient object detection. Using the DUTS dataset, images were augmented with horizontal flipping and random rotations at arbitrary angles. Salient object detection with convolutional neural networks (CNNs) incorporating attention mechanisms effectively focuses on both high- and low-level features. This approach has demonstrated competitive performance in detecting salient objects, and the performance of different attention mechanisms has been evaluated. Saliency object detection was performed using different deep learning models, including VGG-16, ResNet-50, and DenseNet-169.

Keywords

• Salient object detection
• deep learning
• image processing

Derin Öğrenme ile 2D Görüntülerde Belirgin Nesne Tespiti

Abstract

Belirginlik tespiti, bir görüntü veya bir görüntü içindeki bir bölgenin diğerlerinden ne kadar farklı veya dikkat çekici olduğunu belirleme işlemidir. Bu çalışmada, belirginlik tespiti için derin öğrenme yöntemleri ve görüntü işleme teknikleri kullanılmıştır. DUTS veri kümesi kullanılarak yapılan çalışmada; görüntülere yatay çevirme, rastgele açılarda döndürme gibi veri artırma işlemleri uygulanmıştır. Çalışmada dikkat mekanizmaları kullanılarak gerçekleştirilen Evrişimli Sinir Ağları ile belirginlik tespiti, yüksek ve düşük seviyeli özelliklere etkili bir şekilde odaklanmayı sağlamaktadır. Bu yaklaşım, belirgin nesnelerin tespiti konusunda başarılı sonuçlar elde edilmesini sağlamış ve farklı dikkat mekanizmalarının performansı değerlendirilmiştir. Çalışma kapsamında VGG-16, ResNet-50, DenseNet-169 gibi farklı derin öğrenme modellerinde belirgin nesne tespiti yapılmıştır.

Keywords

• Belirgin nesne tespiti
• derin öğrenme
• görüntü işleme

Supporting Institution

Bu çalışma için herhangi bir kurum ve/veya kuruluştan destek alınmamıştır.

Ethical Statement

Yazarlar çalışmanın, etik kurul izni veya herhangi bir özel izin gerektirmediğini beyan etmektedir.

Thanks

Bu makale Gönül Sinem Özdoğan’ın Yüksek Lisans tez çalışması kapsamında yapılmıştır.

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