摘要
由于布匹的花纹样式多,图案复杂,导致瑕疵检测困难等问题,给出了一种基于学习的Gabor滤波器实现多样式布匹瑕疵的检测方法.该方法首先利用多方向、多尺度Gabor滤波器对多样式布匹图像滤波,优选最佳滤波尺度.然后通过判别式提取瑕疵特征,对特征图像分块处理,融合优选的特征信息块,最后对融合后的图像进行二值化和形态学滤波处理.实验结果表明,该方法能够有效检测多样式布匹瑕疵,具有较好的实时性.相比于同类算法,检测精度和时间效率均有一定的提高.
Complex cloth patterns make it difficult to detect defects in the manufacturing process.Thus,a learning-based Gabor filter in multi-style fabric defect detection is proposed.First,Gabor filters are used to filter multi-style cloth at different directions and scales and select the optimal filter scale.Then,the defect features are extracted by using digital image feature discriminant,the feature images are segmented,and a number of preferred feature information blocks are merged.Finally,the fused images are processed by binarized and morphological filtering.Experimental results show that the method performs effectively both in flaw de-tecting of multi-style cloth and in real-time performing.Compared with similar algorithms,the detection accuracy and time efficiency have a certain increase.
出处
《西安工程大学学报》
CAS
2017年第6期775-780,共6页
Journal of Xi’an Polytechnic University
基金
国家自然科学基金资助项目(61471161)
陕西省教育厅自然科学基金资助项目(15JK1305)
