Abstract: In order to efficiently detect defect for fabric image with complex texture and variety of defects, this paper proposed a novel defect detection algorithm based on local statistical features and global saliency analysis. In the proposed algorithm, the target image is first divided into blocks with the same size, then the LBP technique is used to extract the texture features of the blocks and the histogram technique is used to extract the grayscale statistical features of the blocks. Second, for a given image block, K blocks are randomly chosen for calculating the LBP feature contrast and grayscale histogram feature contrast between the given block and the randomly-chosen blocks. Based on the obtained global contrast information, a saliency map is produced. Finally, the saliency map is segmented by using an optimal threshold, which is obtained by an iterative approach. Through these procedures, the detection result is obtained. The experimental results demonstrate that the proposed algorithm, integrating the local textual and grayscale statistical features and the global saliency analysis, can detect the fabric defections effectively.