Deep Endoscope: Intelligent Duct Inspection for the Avionic Industry

• Published in: IEEE transactions on industrial informatics 2018-04, Vol.14 (4), p.1701-1711
• Main Authors: Martelli, Samuele, Mazzei, Luca, Canali, Carlo, Guardiani, Paolo, Giunta, Salvatore, Ghiazza, Alberto, Mondino, Ivan, Cannella, Ferdinando, Murino, Vittorio, Bue, Alessio Del
• Format: Article
• Language: English
• Subjects: Avionics; Deep learning; Ducts; Endoscopes; Image analysis; Image detection; Inspection; Machining; Probes; Robot arms; robotic endoscope; Robots; Task analysis; Transmissions (machine elements); visual inspection; Visualization
• ISSN: 1551-3203; 1941-0050
• DOI: 10.1109/TII.2018.2807797

We present the first autonomous endoscope for the visual inspection of very small ducts and cavities, up to a 6-mm diameter. The system has been designed, implemented, and tested in a challenging industrial scenario and in strict collaboration with an avionic industry partner. The inspected objects are metallic gearboxes eventually presenting different residuals (e.g., sand, machining swarfs, and metallic dust) inside the oil ducts. The automatic system is actuated by a robotic arm that moves the endoscope with a microcamera inside the gearbox duct, while a deep-learning-based spatio-temporal image analysis module detects, classifies, and localizes defects in real time. Feedback is given to the robotic arm in order to move or extract the endoscope given the detected anomalies. Evaluation provides a detection rate of nearly 98 % given different tests with different types of residuals and duct structures.