Fabric defect segmentation is integral to textile quality control. Despite this, the scarcity of high-quality annotated data and the diversity of fabric defects present significant challenges to the application of deep learning in this field. These factors limit the generalization and segmentation performance of existing models, impeding their ability to handle the complexity of diverse fabric types and defects. To overcome these obstacles, this study introduces an innovative method to infuse specialized knowledge of fabric defects into the Segment Anything Model (SAM), a large-scale visual model. By introducing and training a unique set of fabric defect-related parameters, this approach seamlessly integrates domain-specific knowledge into SAM without the need for extensive modifications to the pre-existing model parameters. The revamped SAM model leverages generalized image understanding learned from large-scale natural image datasets while incorporating fabric defect-specific knowledge, ensuring its proficiency in fabric defect segmentation tasks. The experimental results reveal a significant improvement in the model's segmentation performance, attributable to this novel amalgamation of generic and fabric-specific knowledge. When benchmarking against popular existing segmentation models across three datasets, our proposed model demonstrates a substantial leap in performance. Its impressive results in cross-dataset comparisons and few-shot learning experiments further demonstrate its potential for practical applications in textile quality control.

翻译：织物缺陷分割是纺织品质量控制的重要组成部分。然而，高质量标注数据的稀缺性以及织物缺陷的多样性，给深度学习在该领域的应用带来了重大挑战。这些因素限制了现有模型的泛化能力和分割性能，使其难以应对复杂多样的织物类型和缺陷。为克服这些障碍，本研究提出了一种创新方法，将织物缺陷的专业知识注入到大规模视觉模型Segment Anything Model (SAM) 中。通过引入并训练一套独特的与织物缺陷相关的参数，该方法无缝地将领域特定知识整合到SAM中，而无需对预训练模型参数进行大规模修改。改进后的SAM模型在利用从大规模自然图像数据集中学到的通用图像理解能力的同时，融入了织物缺陷的特定知识，从而确保其在织物缺陷分割任务中的专业性。实验结果表明，这种通用知识与织物特定知识的新颖融合显著提升了模型的分割性能。在与三个数据集上的现有流行分割模型进行基准测试时，我们提出的模型在性能上实现了大幅飞跃。其在跨数据集比较和少样本学习实验中的出色表现，进一步证明了其在纺织品质量控制实际应用中的潜力。