Furniture Image Recognition with CBAM-Enhanced MobileNet-Based Deep Learning: A Lightweight and Efficient Approach

Abstract

Automatic recognition and classification of furniture images is a difficult problem due to the high visual similarity between classes, complex shape-texture structures, and limited data diversity. In this study, we propose a lightweight, fast, and mobile-friendly deep learning framework to solve this problem. The proposed method integrates the Convolutional Block Attention Module (CBAM) into MobileNet-based architectures, aiming to both focus on critical regions and increase feature extraction capacity. Furthermore, data augmentation strategies and multi-scale representation are applied to enhance the generalization performance of the models. Experimental studies are conducted on a publicly available image dataset consisting of 32 different furniture classes. The results show that the MobileNetV1 model optimized with CBAM delivers the highest performance with an accuracy rate of 99.78%. While MobileNetV1 achieved 98.71% accuracy in its pure form, the accuracy dropped to 98.44% after data augmentation. Integrating CBAM increased the accuracy to 99.78%, but the combined use of CBAM and data augmentation yielded a relatively lower success rate of 99.57%. The MobileNetV2 model also demonstrated stable performance with CBAM and data augmentation, with 99.46% accuracy. The NASNetMobile model, on the other hand, achieved consistent success with CBAM, with 98.71% accuracy. These findings demonstrate the critical importance of model architecture, attention mechanisms, and data diversity in furniture image recognition. Furthermore, the low computational cost of the proposed approach demonstrates its suitability for real-time applications in mobile and embedded systems.

Keywords

• Furniture classification
• Artificial intelligence
• Deep learning
• CNN
• Woodworking

CBAM Destekli Mobilenet Tabanlı Derin Öğrenme İle Mobilya Görüntü Tanıma: Hafif ve Etkin Bir Yaklaşım

Abstract

Mobilya görüntülerinin otomatik olarak tanınması ve sınıflandırılması; sınıflar arasındaki yüksek görsel benzerlik, karmaşık şekil–doku yapıları ve veri çeşitliliğinin sınırlı olması nedeniyle zor bir problemdir. Bu çalışmada söz konusu problemi çözmek amacıyla hafif, hızlı ve mobil cihazlarda çalışmaya uygun bir derin öğrenme yapısı önerilmiştir. Önerilen yöntemde, MobileNet tabanlı mimarilere Convolutional Block Attention Module (CBAM) entegre edilerek, modelin hem kritik bölgelere odaklanması hem de özellik çıkarım kapasitesinin artırılması hedeflenmiştir. Ayrıca veri artırma stratejileri ve çok ölçekli temsil gücü, modellerin genelleme performansını güçlendirmek için uygulanmıştır. Deneysel çalışmalar, 32 farklı mobilya sınıfından oluşan kamuya açık bir görüntü veri kümesi üzerinde gerçekleştirilmiştir. Elde edilen sonuçlar, CBAM ile optimize edilen MobileNetV1 modelinin %99,78 doğruluk oranıyla en yüksek performansı sunduğunu göstermektedir. MobileNetV1 yalın hâlde %98,71 doğruluk sağlarken, veri artırma uygulaması sonrasında doğruluk %98,44’e düşmüştür. CBAM entegrasyonu doğruluğu %99,78’e yükseltmiş, ancak CBAM ve veri artırmanın birlikte kullanımı %99,57 ile nispeten daha düşük bir başarı sergilemiştir. MobileNetV2 modeli de CBAM ve veri artırma ile %99,46 doğrulukla dengeli bir performans göstermiştir. NASNetMobile modeli ise CBAM ile %98,71 doğrulukla istikrarlı bir başarı sunmuştur. Bu bulgular, model mimarisi, dikkat mekanizmaları ve veri çeşitliliğinin mobilya görüntü tanımada kritik öneme sahip olduğunu ortaya koymaktadır. Ayrıca önerilen yaklaşımın düşük hesaplama maliyeti, yöntemin mobil ve gömülü sistemlerde gerçek zamanlı uygulamalar için uygun olduğunu göstermektedir.

Keywords

• Mobilya sınıflandırma
• Yapay zeka
• Derin öğrenme
• ESA
• Ağaç işleri

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