Fast Blob and Air Line Defects Detection for High Speed Glass Tube Production Lines

• Published in: Journal of imaging Vol. 7; no. 11; p. 223
• Main Authors: De Vitis, Gabriele Antonio, Di Tecco, Antonio, Foglia, Pierfrancesco, Prete, Cosimo Antonio
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 25-10-2021; MDPI
• Subjects: Accuracy; Adaptive algorithms; Algorithms; Assembly lines; Cameras; Classification; defect detection; Defects; Dislocations; image processing; Inspection; inspection systems; Luminous intensity; Machine vision; Mathematical analysis; pharmaceutical glass tube; Pharmaceuticals; Production lines; real time inspection; Reduction; Software; Tubes; Vision systems
• ISSN: 2313-433X; 2313-433X
• DOI: 10.3390/jimaging7110223

During the production of pharmaceutical glass tubes, a machine-vision based inspection system can be utilized to perform the high-quality check required by the process. The necessity to improve detection accuracy, and increase production speed determines the need for fast solutions for defects detection. Solutions proposed in literature cannot be efficiently exploited due to specific factors that characterize the production process. In this work, we have derived an algorithm that does not change the detection quality compared to state-of-the-art proposals, but does determine a drastic reduction in the processing time. The algorithm utilizes an adaptive threshold based on the Sigma Rule to detect blobs, and applies a threshold to the variation of luminous intensity along a row to detect air lines. These solutions limit the detection effects due to the tube’s curvature, and rotation and vibration of the tube, which characterize glass tube production. The algorithm has been compared with state-of-the-art solutions. The results demonstrate that, with the algorithm proposed, the processing time of the detection phase is reduced by 86%, with an increase in throughput of 268%, achieving greater accuracy in detection. Performance is further improved by adopting Region of Interest reduction techniques. Moreover, we have developed a tuning procedure to determine the algorithm’s parameters in the production batch change. We assessed the performance of the algorithm in a real environment using the “certification” functionality of the machine. Furthermore, we observed that out of 1000 discarded tubes, nine should not have been discarded and a further seven should have been discarded.