Abstract: In order to address the limitations of existing tip clearance measurement methods in complex outdoor environments, this paper proposes a real-time measurement method based on deep learning networks. The method utilizes the YOLOv3-tiny network to locate the rotor tip area in collected rotor tip images. The OTSU algorithm is then applied to segment the rotor tip from the background, and the rotor tip contour is extracted to locate the pneumatic center points of the upper and lower rotor tips. The rotor tip clearance is calculated based on the located center points. Experimental results conducted in both simulated and real environments demonstrate the high accuracy of the proposed method. The maximum error in tip clearance measurement is 1.99 mm when the camera is positioned 20 meters away from the tip. The proposed method has been successfully applied in real experiments involving tip clearance measurements under various complex background and illumination conditions, where the method exhibits strong adaptability and fast processing speed with a frame rate of 50 fps.

Key words: machine vision, complex environment, twin-rotor helicopter, measurement of tip clearance