Deep Endoscope: Intelligent Duct Inspection for the Avionic Industry

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...

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Bibliographic Details
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
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Description
Summary: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.
ISSN:1551-3203
1941-0050
DOI:10.1109/TII.2018.2807797