Fuzzy Optimization Feature Fusion for Enhanced Fine-Grained Visual Classification in Sustainable Manufacturing Using Vision Transformer

Fine-grained visual classification (FGVC) in sustainable manufacturing faces challenges due to the diverse, complex, and highly similar objects in manufacturing environments. Traditional convolutional neural networks often require extensive annotations and high computational costs, limiting their effectiveness. This study introduces a fuzzy optimization feature fusion model (FOFFM) based on vision transformer, designed to enhance FGVC accuracy and efficiency. FOFFM addresses challenges, such as information loss during image-to-token mapping and high category similarity, by optimizing classification token capabilities and leveraging contrastive loss. By enhancing resource efficiency and reducing redundant computations, FOFFM contributes to lower energy consumption and operational costs, directly supporting sustainable manufacturing practices. Experimental results on the NABirds dataset demonstrate FOFFM's competitive performance with a streamlined, resource-efficient end-to-end training process. Unlike other methods, such as TransFG, FOFFM reduces computational complexity while maintaining robust accuracy, making it highly suitable for practical applications in sustainable manufacturing, particularly in optimizing resource utilization and minimizing environmental impact. This work provides valuable insights for manufacturing data analysis and contributes to advancing FGVC in sustainable manufacturing contexts.