Abstract: 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.

Key words: rubber plunger, defect detection, super pixel segmentation, random forest