基于机器视觉的轴承座分类识别系统研究

Recognition system of bearing house based on machine vision

针对轴承座零件人工分拣效率低、出错率高等问题,对工业相机标定、轴承座图像采集、混合去燥、轮廓拟合、特征比对与目标分类方法展开了研究,提出了一种基于机器视觉的轴承座分类识别系统。首先搭建了一套图像采集系统来采集工业照明背景下多个轴承座摆放的图像,利用中值滤波对这些图像进行了去噪处理,将图像二值化后再利用特征过滤进一步对图像进行了混合去噪处理,遍历拟合了各轴承座轮廓并求出了其最小外包矩形,通过仿射变换将轴承座图像摆正,最后再根据3种轴承座的具体特征设置了ROI区域,依次对其进行了分类筛选,利用爱普生机器人进行了分类抓取实验。实验结果表明:该机器视觉分类识别系统总体识别准确率高达99.967%,相比于传统人工分拣有较大提高,可满足工业生产要求。

Aiming at the problems of low efficiency and high error rate in artificial classification of bearing house,the industrial camera calibration,image acquisition of bearing house, hybrid denoising,feature recognition and target classification were studied.At first,the images of bearing houses were collected under industrial lighting by image acquisition system.Median filter was designed to filter the noise of images. Feature filtering was used for further hybrid denoising of images after binaryzation processing. Then, bearing houses contours were fitted and the minimum outsourcing rectangle were calculated,these images were put straight by affine transformation.Finally, they were categorized according to the specific characteristics, and the capture experiment was carried out by EPSON robot. The results indicate that the overall recognition accuracy of the method is up to 99.967%, which is better than traditional manual detection. The system can meet the requirements of industrial production.