Aiming at the problem of misdetection caused by complex texture during the defect detection process of mesh fabric, a structured matrix decomposition method for mesh fabric defect detection is proposed. First, the image is enhanced by the Retinex algorithm, the feature matrix is generated using the extracted underlying image features, and it is decomposed into a low-rank matrix containing fabric image background information and a sparse matrix containing defect information. Secondly, the enhanced image is used to obtain Advanced priori matrix and index tree to achieve significant enhancement of defects. By calculating the value of the sparse matrix, the saliency of the defect is obtained. Finally, the defect saliency map is segmented by the optimal threshold segmentation algorithm to obtain the defect detection result. The performance of the algorithm is verified by using the defect images of the mesh fabric collected by the public data set TILDA and the CCD industrial camera. The results show that compared with other algorithms, the recognition accuracy of this algorithm reaches 94. 25%, the recall rate reaches 92. 48%, and the classification accuracy rate reaches 90. 12%.