Abstract
The tire defect detection method can help the rehabilitation robot to achieve autonomous positioning function and improve the accuracy of the robot system behavior. Defects such as foreign matter sidewall, foreign matter tread, and sidewall bubbles will appear in the process of tire production, which will directly or indirectly affect the service life of the tire. Therefore, a novel and efficient tire defect detection method was proposed based on Faster R-CNN. At preprocessing stage, the Laplace operator and the homomorphic filter were used to sharpen and enhance the data set, the gray values of the image target and the background were significantly different, which improved the detection accuracy. Moreover, data expansion was used to increase the number of images and improve the robustness of the algorithm. To promote the accuracy of the position detection and identification, the proposed method combined the convolution features of the third layer and the convolution features of the fifth layer in the ZF network (a kind of convolution neural network). Then, the improved ZF network was used to extract deep characteristics as inputs for Faster R-CNN. From the experiment, the proposed faster R-CNN defect detection method can accurately classify and locate the tire X-ray image defects, and the average test recognition rate is up to 95.4%. Moreover, if there are additional types of defects that need to be detected, then a new detection model can be obtained by fine-tuning the network.