Abstract: As the fabric defect type of diversity and traditional artificial detection methods inefficient ,in order to detect the fabric defect more effective, A new approach, SGE, based on saliency region feature for fabric defect detection is studied. In this approach, the original image is divided into two parts, one extracts the saliency region feature of fabric defect by improved FSR roughly, another employing the gabor filter and taking the amplitude as an output characteristics, and extracts the saliency region feature of fabric defect by PSR accurately, then by using maximum entropy to segment the saliency region respectively and fused the sub-images. The result is get got by calculating perimeter and area of the contours to removal the isolated points. The experiment selects four types of typical fabric defect images and OpenCV library is used. The experiment result shows that the algorithm, without prior learning，meet the real-time.

Key words: Defect detection, Saliency region feature, Gabor filter, Maximum entropy, OpenCV

摘要： 鉴于织物疵点类型的多样性和传统人工检测方法的低效率，为更有效地检测织物疵点，提出一种新的基于图像显著性特征的织物疵点检测方法——SGE。将原织物图分成相同两份:一份利用改进的基于频率的显著性区域(FSR)方法提取区域特征，粗定位疵点位置。另一份先Gabor滤波，取Gabor模图为输出特征；再利用基于像素的显著性区域(PSR)方法进行区域特征提取，细定位疵点位置;然后利用最大熵分别对粗细定位的疵点图进行分割，再融合;最后描绘轮廓,计算周长和面积,去除孤立点,得最终检测结果。采用OpenCV算法库，选取了4种具有代表的织物疵点图片进行验证。实验结果表明，这种粗细定位疵点的方法能够获得较好的检测结果，无需事先学习，能够满足实时性要求。

关键词: 疵点检测, 显著性区域特征, Gabor滤波器, 最大熵, OpenCV