应用显著性算法的纱线条干均匀度检测

Yarn evenness detection based on saliency algorithm

针对运用图像方法进行纱线条干均匀度检测时,背景黑板、纱线毛羽以及图像噪声等对检测结果影响较大的问题,借鉴人的视觉感知机制,提出一种应用显著性算法检测纱线条干均匀度的方法。对采集到的纱线图像提取颜色和亮度特征,进行显著性分析,突出纱线条干区域,然后利用迭代阈值分割算法和区域滤波,得到准确清晰的纱线条干二值图像,基于此进行直径计算、均匀度分析和纱线疵点判定。通过边缘准确性评价可知,采用所提方法分割得到的纱线条干二值图像有着较高的分割精度。通过与Uster Classimat 5的均匀度检测结果进行比较,证明这种方法可得到准确的结果,与Uster Classimat 5的测量结果有着较好的一致性。

When the image processing method is used to detect the yarn evenness, the background blackboard as well as the yarn hairiness and the image noise would have great influence on the detection results. To solve this problem, a method referring to the human visual perception mechanism for detecting yarn evenness based on saliency algorithm was proposed. Firstly, the color and brightness features were extracted from the collected yarn images saliency of for saliency analysis to highlight the yarn evenness area. Then the iterative threshold segmentation algorithm and the area filtering were adopted to obtain accurate and clear yarn evenness binary images. Based on the binary images, the diameter and yarn evenness were calculated, and the yarn defect was determined. The edge accuracy evaluation shows that the proposed method of saliency analysis can obtain the yarn evenness binary images with better segmentation. Compared with the evenness detection result of the Uster Classimat 5, the results obtained by the method are accurate and have a good consistency with those of the Uster Classimat 5.