Abstract
Engine blades, being critical components of aircraft engines, pose a substantial threat to both the engine and the entire aircraft if they fracture during flight. Hence, inspecting and maintaining these blades are crucial to ensuring flight safety. In the process of blade damage detection, personnel typically utilize borescope inspection equipment to manually examine each blade and count them as they pass, thereby guaranteeing the examination of every individual blade within the engine to prevent any missed or duplicate inspections. This paper presents a new video interpretation method applied to the scenario of engine blade counting. The core of this algorithm involves employing the cosine correlation function to calculate the similarity between video frames captured during borescope inspections, followed by adaptively thresholding the processed signal for dynamic binarization, and ultimately counting the falling edges. By adopting frame-related approaches instead of relying on local image characteristics, this algorithm exhibits high robustness against smooth blade surfaces and metallic reflections. Additionally, it efficiently manages motion blur and directional variations that occur during the rapid movement of the blades. Compared to existing methods, this algorithm requires minimal training time, is compatible with various turbine engine blades, and guarantees real-time count updates.