Deep learning-based novel aluminum furniture design style recognition and key technology research

Abstract This study explores a pioneering research effort focusing on the use of deep learning techniques to achieve high-precision automatic recognition of aluminum furniture design styles, and proposes an innovative convolutional neural network (CNN) architecture that deeply integrates the migration learning techniques of pre-trained models and the multi-level feature of the feature pyramid network (FPN) integration mechanism. This research is dedicated to solving the challenges of recognizing design elements and styles in the aluminum furniture industry, especially the robustness of recognition under different size variations, complex background interference, and diverse design styles. First, this study fills the gap of deep learning in the field of automatic classification of aluminum furniture design styles, using the powerful image understanding and pattern recognition capabilities of deep learning to effectively break through the bottleneck of the previous traditional methods that have low recognition accuracy when dealing with complex shapes, detail-rich, and diverse styles of aluminum furniture. This is the first time that deep learning technology is systematically applied to such specific scenarios, showing significantly better performance than traditional recognition means. Second, the core contribution of this study is the design of a comprehensive integration scheme that creatively combines a pre-trained CNN model and an FPN structure. This composite deep learning model is able to take full advantage of the generic feature representation acquired by the pre-trained model on large-scale image datasets, while extracting multi-scale local and global features with the advantage of FPNs, which ensures that key design style features can be accurately captured no matter how the size of the design elements of aluminum furniture changes.