Detection of chemical fabric defects on the basis of morphological processing

Complex computation and the poor adaptability of ambient light are common problems of the algorithm for fabric defect detection. In this paper, we propose a new fabric defect detection approach based on a morphological filter. First, the structural nodes are analyzed and structural features are extracted for selection of the structural elements. Second, the grayscale morphological operations and top-hat transformation are carried out to highlight the defective areas. At the same time, the phenomenon of uneven brightness distribution is greatly reduced. Power transformation and thresholding are then combined to segment the defective areas. This scheme can detect the defects precisely while reducing both the complexity of the algorithm and the interference of the ambient light. The results of the experiments show that the proposed approach is effective and feasible.