| Autor: |
Yan, Yu, Tan, Yanjun, Gao, Pengfu, Yu, Qiuyu, Deng, Yuntao |
| Předmět: |
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| Zdroj: |
Textile Research Journal; Nov2024, Vol. 94 Issue 21/22, p2434-2444, 11p |
| Abstrakt: |
The initial step in fabric pilling rating is to segment and localize the pilling region, which is a crucial and challenging task. This paper presents a fabric puckering image segmentation method that is integrated into a U-Net network with a dual-attention mechanism. We have enhanced the fully convolutional neural network (U-Net) model by incorporating the dual-attention mechanism. This modification has resulted in a powerful feature extraction capability, enabling the objective and accurate segmentation of the fabric puckering region. We refer to this improved model as the dual-attention U-Net. The network model for fabric pilling feature extraction adopts the improved VGG16 model architecture as its encoding part. The model parameters are initialized with VGG16 pre-training weights to accelerate convergence speed. Second, the feature fusion between the corresponding layers of the encoding part and the decoding part was fed into the dual-attention mechanism module to strengthen the weight values of the fabric pilling region adaptively, which made the model more focused on the fabric pilling target region; Third, the dual-attention U-Net model was trained by data augmentation and migration learning strategies to prevent overfitting; Finally, the performance of the model was evaluated with the collected fabric pilling data set. The results of the experiments indicate that the claimed dual-attention U-Net model is superior to the typical U-Net model in terms of accuracy and precision. The dual-attention U-Net model achieved 99.03% accuracy for fabric pilling segmentation. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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