基于改进双边滤波的皮革缺陷检测

Leather defect detection based on improved bilateral filtering

为了提高皮革缺陷检测效率,提出了一种基于改进双边滤波的皮革缺陷检测算法。通过搭建机器视觉检测平台,完成不同种类缺陷的皮革样本的图像采集,采用改进的双边滤波算法处理样本图像,模糊皮革背景纹理并保留缺陷边缘轮廓,在此基础上,计算各类缺陷的4种特征参量作为输入向量,构建了最小二乘支持向量机自动识别模型。结果表明,与聚类分析算法、阈值分割法和小波分析法相比,本文中采用的算法能更高效地检测出皮革多种缺陷,检测平均用时0.83s,缺陷检测准确率为93.3%。此研究结果为皮革的实时检测提供了有效途径。

In order to improve the efficiency of leather defect detection,a leather defect detection algorithm based on improved bilateral filtering was proposed.Through constructing machine vision detection platform,different kinds of defects in the finished leather sample image were obtained,sample images were processed with improved bilateral filtering algorithms to make the leather background texture fuzzy and keep its defect edge profile.Then,various kinds of defects of four characteristic parameters were calculated as the input vector,and the automatic identification of least squares support vector machine(SVM)mode was constructed.The results showed that compared with cluster analysis algorithm,threshold segmentation algorithm and wavelet analysis algorithm,the algorithm adopted in this paper could detect various defects of leather more efficiently.The average detection time was 0.83s,and the accuracy of defect detection was 93.3%.The results provide an effective way for real-time leather detection.