| dc.description.abstract | Abstract. The identification of knitted fabric patterns is a critical component in modern textile manufacturing processes. Traditionally, manual identification remains the predominant method, but it often incurs significant labor costs and suffers from low efficiency. Machine learning-based approaches, though promising, require labor-intensive feature engineering and fail to achieve satisfactory accuracy. To address these limitations, we propose the frequency domain weighted DenseNet (FW-DenseNet), a novel architecture tailored for fabric pattern recognition. The conventional DenseNet architecture accumulates features across channel dimensions, often leading to redundancy. In our approach, each channel in DenseNet is assigned a learnable weight, enabling the model to selectively prioritize meaningful feature maps while disregarding redundant information. This design not only minimizes information redundancy but also expands the model’s search space for optimal features. Given that the distinct textures in knitted fabric patterns arise from variations in weaving techniques, capturing detailed texture information is paramount. The frequency domain provides richer and more comprehensive descriptions of texture, making it particularly effective for capturing fine-grained details. Accordingly, we convert knitted fabric images into the frequency domain for feature extraction, ensuring robust texture representation. To address the scarcity of publicly available datasets for knitted fabrics, we constructed a custom dataset for this study. Experimental results demonstrate that FW-DenseNet outperforms existing methods, effectively mitigating the impact of factors such as rotation, positional shifts, and lighting variations, while achieving high accuracy in fabric recognition. | |
