基于区域生长法的自适应图像分割的网眼织物瑕疵检测

Defect detection of eyelet fabric using adaptive image segmentation based on region growing method

为了解决网眼织物在织造过程中的实时检测问题,提出了一种基于频域滤波和区域生长法的瑕疵区域自适应分割方法。通过分析傅里叶变换后的功率谱获得网眼织物纱线分布特征后,通过设计滤波器自动滤除了与织物纹理背景相关的频率成分以削弱织物自身纱线分布结构对瑕疵检测精度的干扰。利用滤波后图像的灰度分布特点自适应地求取了分割参数,并使用区域生长法实现了对瑕疵区域的自动分割。利用对获得的二值图像进行形态学运算,得到了最终的瑕疵区域分割图像。实验结果表明,该算法能较准确地对瑕疵进行定位,瑕疵区域分割精度较高,自适应性和抗干扰能力较强,可以满足工业检测的要求。

Aiming at solving the problem of real-time defect detection of eyelet fabric,an adaptive image segmentation method based on the combination of frequency domain filtering and region growing method was proposed. The power spectrum obtained from Fourier transform was analyzed,and then the yarn distribution characteristics of eyelet fabric were obtained. Specific frequency component associated with background texture was filtered and its effect on detection precision was weakened. An adaptive region growing method was employed to extract defective regions from homogeneous background in the reconstructed image. Two main parameters were automatically determined by the gray level distribution of filtered image. Morphological operations were applied to the binary image to identify defects and eventually,yield noisefree images of the defects were obtained. The experimental results indicate that the adaptive and robust algorithm could localize the defect with high segmentation accuracy and therefore,it could meet the needs of industrial detection requirements.