Abstract:

 A tunnel manual inspection auxiliary system is designed based on augmented reality(AR) technology to achieve highquality and highefficiency tunnel inspection. The virtual data are applied to the real scenario using the AR terminal to analyze, guide, and make decisions on tunnel inspection, thus effectively assisting manual tunnel inspection. The scale invariance and efficient recognition performance of speededup robust features(SURF) are used to identify and match electromechanical equipment. Lowe′s algorithm is used to ensure the best matching effect. Recognition and matching based on the unique properties of the target object are established using data from tunnel 5G indoor highprecision positioning, object′s point of interface information, AR intelligent terminal direction, and angle sensor. Finally, the particle swarm optimization information and algorithm are used to improve the correlation and fusion of the SURF algorithm and unique attribute recognition and matching results, which considerably improve the recognition accuracy and matching. The actual operation effect is compared with the traditional manual inspection effect from the matching performance and AR rendering performance perspectives, confirming that the AR inspection auxiliary system has certain advantages over manual inspection. Furthermore, the realtime equipment data and decisionmaking functions displayed in the system reduce the average inspection time by 46% compared to the traditional manual inspection. Thus, the inspection route planned by AR auxiliary inspection system can effectively avoid missing inspections. The overall inspection accuracy is approximately 94%, which effectively improves the quality and efficiency of the inspection.

Key words: tunnel engineering, auxiliary system, augmented reality technology, tunnel equipment, speededup robust features