基于超像素分割和随机森林的橡胶柱塞缺陷检测方法

针对直径为3 mm的小尺寸橡胶柱塞件端面，其受光斑、灰尘及纹理干扰不易分割提取缺陷轮廓的问题，提出一种结合SLIC（简单线性迭代聚类）和RF（随机森林）算法的缺陷检测系统。首先利用霍夫变换和各向异性扩散滤波对图像预处理，然后采用基于超像素分割的SLIC算法分割和提取缺陷区域，最后把获得的缺陷区域的五维形状特征作为RF分类器特征向量进行缺陷分类预测。结果表明，SLIC算法较传统的自适应阈值分割算法快了0.128 s，并且分割效果远好于传统算法，能够准确分割出小至0.5 mm的缺陷，整体检测流程平均耗时小于1.5 s，同时RF分类结果准确率达到97.3%。因此，本文的缺陷检测系统满足在线检测准确性和实时性的要求，可在实际工作中使用。

Rubber Plunger Defect Detection Method #br# Based on Super Pixel Segmentation and Random Forest

For the small-sized rubber plunger end face with a diameter of 3 mm, it was difficult to segment the defect contour by the interference of light spots, dust and texture, So a defect detection system combining SLIC(simple linear iterative clustering) and RF(random forest) algorithms was proposed. Firstly, Hough transform and anisotropic filtering were used as image preprocessing. Then SLIC algorithm based on super pixel segmentation was used to segment and extract defect regions. Finally, the five-dimensional shape feature of the obtained defect area was used as the RF classifier feature vector for defect classification prediction. The results show that the SLIC algorithm is 0.128 s faster than the traditional adaptive threshold segmentation algorithm, and the segmentation effect is much better than the traditional algorithm, the defects as small as 0.5 mm can be accurately segmented, the overall inspection process takes less than 1.5 s on average. At the same time, the accuracy rate of RF classification is 97.3%. Therefore, the defect detection system of this paper meets the requirements of accuracy and real-time of online detection, which can be used in practical work.