基于纹理知识引导的跨域织物缺陷检测

Detection of fabric defects is an indispensable step in textile production. However, obtaining large amounts of annotated data in practical situations is difficult. The unsupervised domain adaptation method provides an effective solution to this problem, which can improve the model performance without target domain data annotation. However, although existing methods perform well on classical datasets, their model performance decreases significantly when applied to more complex and textured fabric defect detection tasks. To address this issue, a Texture Knowledge Guided (TKG) cross-domain fabric defect detection method is proposed to enhance the detection performance of the object detection transfer model on fabric images. The TKG method comprises three key components: texture enhancement, joint attention, and consistency-adversarial modules. The texture enhancement module enhances the texture information in the input image via Fourier transform, enabling the model to better capture complex texture features. The joint attention module introduces an attention mechanism that can capture more comprehensive texture and structural information. By adaptively adjusting the weights of different regions and channels, it enhances the attention of the model to key textures and defect areas. The consistency-adversarial module enhances the adaptability of the model to target domain data via consensus training and adversarial training, improving the detection performance of the model in the target domain. The experimental results show that compared with the comparative methods, the TKG method exhibits significant superiority in fabric defect target detection tasks. In the cross-domain detection experiment from twill to plain weave, the TKG method achieves a performance improvement of up to 3.1 percentage points in mAP@0.5, reflecting the excellent cross-domain defect detection capability of this method using actual fabric production environment data.