Research on furniture image classification based on MobileNetNAK

With the rapid development of the furniture industry, automatic classification of furniture images has become an important research area. However, this task faces several challenges, including complex image backgrounds, diverse furniture types, and varying forms. To address these issues, we propose a novel furniture image classification method, MobileNetNAK, based on the MobileNetV3 network. First, the method integrates a non-local attention module to capture non-local dependencies within images, significantly enhancing the model’s ability to extract key information. Second, the Adamax optimizer is employed to train the model. By adaptively adjusting the learning rate, it accelerates convergence and reduces the risk of overfitting. Third, the Kolmogorov–Arnold networks method is incorporated to decompose complex convolution operations into multiple simpler ones, thereby improving computational efficiency and feature extraction capabilities. Experimental results demonstrate that MobileNetNAK significantly improves classification performance in furniture image tasks. On Dataset 1, the model achieves improvements of 6.7%, 6.6%, 6.6%, and 6.6% in accuracy, precision, recall, and F1-score, respectively, compared to the baseline. On Dataset 2, the corresponding improvements are 2.7%, 2.4%, 2.7%, and 2.9%. Additionally, the model maintains a high inference speed of 147.80 fps, balancing performance with computational efficiency. These results highlight the strong adaptability and deployment potential of MobileNetNAK in multi-category and fine-grained furniture image classification tasks, offering a novel and effective solution for this domain.