目的 为了提高色织物疵点检测的准确率。方法 提出一种基于高斯金字塔和视觉显著性的色织物疵点检测方法。首先预处理待检测色织物图像，削弱不均匀光照和环境造成的影响；再对预处理后的图像进行灰度化，接着对灰度图进行高斯金字塔分层，然后对分层后的图像进行显著性处理，以获取图像的显著图；最后利用迭代阈值分割的方法对显著图进行阈值分割，得到色织物图像的疵点区域。结果 将该方法与其他色织物疵点检测方法进行对比可知，检测的效果明显优于其他方法，疵点检测的准确率为91.25%。结论 该方法可以有效地对色织物疵点进行检测，将疵点区域提取出来，为色织物后续生产中的加工处理提供有用信息。

The work aims to improve the defect detection accuracy of yarn-dyed fabric. A defect detection method of yarn-dyed fabric based on Gaussian pyramid and visual saliency was proposed. Firstly, the yarn-dyed fabric image to be detected was preprocessed to reduce the uneven illumination and the environmental influence. Then, the preprocessed image was grayed and layered based on Gaussian pyramid, and the layered images were processed based on saliency to obtain saliency maps. Finally, the saliency maps were segmented by iterative threshold segmentation to obtain the defect region of the yarn-dyed fabric image. Compared with other defect detection methods of yarn-dyed fabric, the detection effect of the proposed method was significantly better than other methods, and the accuracy of defect detection reached 91.25%. The proposed method can effectively detect and extract the defects of the yarn-dyed fabric, which provides useful information for the processing in the subsequent production of the yarn-dyed fabric.