应用积分图的织物瑕疵检测快速算法

为解决现有基于图像处理的织物瑕疵检测算法实时性较差、正确率偏低等问题，提出一种包含学习和检测2个阶段的瑕疵检测算法。通过对无瑕疵模板图像的梯度能量特征及其分布特性的学习，自适应获得检测阶段所需的参数。一方面利用积分图原理将任意大小的图像块内的求和运算化简为三次加法运算，快速提取织物图像的梯度能量特征，实现织物瑕疵的实时检测，另一方面利用核函数拟合特征参数分布，结合均值漂移法求解分布峰值获得自适应的瑕疵判定阈值参数，实现织物瑕疵的准确分割。通过实验将本文算法与现有基于局部二值模式特征、小波特征、规则带特征等算法进行对比，针对包含3种纹理6类瑕疵的织物图像数据集的测试结果显示，本文算法平均处理时间为56ms，正确率为97%。

Fast fabric defect detection algorithm based on integral image

Theexisiting fabric defect detection methods based on image processing is poor in real-timeperformance and low in accuracy. In order to solve this problem, an algorithm consisting of two stages of learning and detection was proposed. By means of learning the gradient energy features and their distribution properties of non-defect model images, parameters in the detection stage were obtained automatically. On the one hand, by using integral image theory, summation operation in the image patch with arbitrary size was simplified to three addition operations, and gradient energy features in fabric images were extracted very quickly, so that fabric defects can be detected in real time. On the other hand, kernel functions were used to fit the distribution of feature parameters, mean shift method was used to solve the peak value in the distribution, and an adaptive threshold was obtained, so that fabric defect can be segmented precisely. In the experiments, the proposed algorithm was compared with the other three methods, respectively, based on local binary pattern features, wavelet features and regular band features. Tests on fabric image datasets including three kinds of textures and six kinds of defects show that the proposed method has an average running time 56 ms and the accuracy rate is 97%.