Rolling Surface Defect Inspection for Drum-Shaped Rollers Based on Deep Learning
Jiamin Tao, Yongjian Zhu, Frank Jiang, Hao Liu, Hongzhan Liu
Abstract
It is difficult to detect defects such as shallow dents and rust on rolling surfaces by using the traditional vision-based surface inspection method (line light source and line array camera mode) which has a low sensitivity to these defects. This paper presents a method that introduces the fringe projection technique for traditional visual inspection devices to overcome the limitations of the traditional methods and uses deep-learning techniques for detecting defects such as cuts, abrasions, dents, and rust on the rolling surfaces of drum-shaped rollers. A new artificial-intelligence-based labeling method, namely, the Padua Incremental Mask Labeling Method, has been introduced for accelerating the calibration process used for defect detection, and based on a one-stage architecture, the You-Only-Look-Once-OurNet (YOLO-OurNet) deep-learning network has been designed for detecting the defects on the rolling surfaces of drum-shaped rollers. From the results of the experimental tests, the time required for detecting a defect has been found to be 0.024s, an accuracy rate of up to 99.2%, and the value of object detection evaluation index F1 of up to 0.988. Our method outperforms the related method on the domain of rolling surface defect detection.