基于多模型级联的双目视觉铸件缺陷检测方法

Casting Defect Detection Method Based on Multi Model Cascade and Binocular Vision

为了解决铸件表面缺陷检测以及缺陷位置的三维定位问题,采用双目视觉系统获得图像,依靠三角测量法获得点云数据,基于正态分布变换配准方法实现工件的三维定位。通过多模型级联解决少样本和样本比例失衡情况下的表面缺陷检测,结合无监督模型的编码解码方式,依靠正样本训练获得对缺陷图像进行修复的网络模型,将修复后的图像与原始图像进行差异分析获得缺陷位置;使用MASK-RCNN模型进行监督模型训练,获得一体化检测分割模型,直接定位缺陷位置及类别。此外还将运动机构的物理坐标系、双目成像的工件坐标系以及平面图像的坐标进行换算,得到多个坐标系的转换关系,实现了平面缺陷的位置信息映射到三维工件的空间中。实验表明提出的双目视觉系统在工件成像以及检测方面具有良好的效果。

In order to realize the surface defect detection and three-dimensional location of casting defects, we obtain the image by binocular vision system, obtain the point cloud data by triangulation method, and normal distribution transformation registration method is used to realize the three-dimensional positioning of the workpiece. Multi model cascading is used to solve the surface defect detection under the condition of few samples and unbalanced sample proportion. Combined with the coding and decoding method of unsupervised model, the network model of defect image repair is obtained by positive sample training, and the defect location is obtained by difference analysis between the repaired image and the original image;the supervised model training is carried out by MASK-RCNN model, and the integration is obtained detect the segmentation model and locate the defect location and category directly. In addition, the physical coordinate system of the moving mechanism, the workpiece coordinate system of binocular imaging and the coordinate of the plane image are converted to obtain the conversion relationship of multiple coordinate systems, and the position information of the plane defect is mapped to the space of the three-dimensional workpiece. Experiments show that this system has good effect in workpiece imaging and detection.