Anomaly Detection of Tire Tiny Text: Mechanism and Method

Due to the variety of tire specifications and poor contrast, it is difficult for ordinary 2D vision-based methods to accurately and autonomously detect anomalies in the tire text embossed on the tire sidewalls. In this paper, a 3D vision-based autonomous scanning mechanism and method is proposed, including three core components: a three-degree-of-freedom (3-DoF) robotic arm, a high-precision turntable and a composite vision probe. Based on the designed vision probe, the mechanism can autonomously scan the tire sidewall through a series of optimal viewpoints to obtain high-quality tire images even in the absence of tire CAD models. To obtain the optimal viewpoints, an information-driven viewpoint planning method is proposed. Combined with the real-time viewpoint adjustment strategy, the viewpoint of the vision probe is dynamically fine-tuned, which can effectively balance the scanning precision and motion cost. In the text anomaly detection phase, a three-stage tire text anomaly detection method based on deep learning and statistics strategy is proposed, which can accurately detect and recognize tire text, identify sub-mold categories, and judge text anomalies. Experimental results show that the proposed mechanism and method perform well in terms of accuracy (
$>$
96%) and efficiency (time per lap for a tire is less than 9 s); it shows that the proposed mechanism and method have certain industrial application value and can be applied to tire quality inspection of on-line production. Note to Practitioners—Tire text may be incorrectly embossed during tire manufacturing. Due to the wide variety of tire specifications, poor contrast, and thin text strokes, it is difficult to detect abnormal tire text. In this paper, a 3D vision-based mechanism and method is developed to address this issue. Using a specially designed composite vision probe and scanning method, the mechanism can observe tiny text information on tires of different specifications from multiple angles and directions. By using our tire text anomaly detection method based deep learning and statistics strategy, the mechanism can accurately and robustly detect the region and content of abnormal text. The mechanism is efficient (with a detection time of less than 9 s per lap for one tire) and accurate (
$>$
96%), indicating that it has great potential in the first inspection or quality inspection step of tire manufacturing in industrial scenarios.