基 于 深 度 学 习 的 结 冰 区 域 和 厚 度 检 测 方 法

Aircraft icing has become one of the biggest threats to safe flight of aircraft，accounting for more than half of military and civil aircraft accidents in the world. Therefore，it is extremely important to detect the icing conditions during flight. Traditional icing sensors can only detect icing information at fixed points and cannot provide feedback on the icing status of overall area. In this paper，a large-area icing detection method is proposed，and a real-time airfoil icing detection model is established by using deep learning algorithm. Airfoil icing area is detected and segmented in real time using the fuselage image captured by cameras as input，and the icing thickness of the airfoil leading edge is calculated. The experimental results from the self-built icing dataset show that the average accuracy of the algorithm is 99. 32%，the mean intersection over union is 93. 20%，the theoretical spatial resolution is approximately 0. 4 mm at a distance of 1. 5 m，the actual measurement error is within 1 mm，and the real-time detection frame rate is 4 frame/s. The experiments demonstrate that this method has excellent detection performance，and potentially lay research foundation for aircraft icing detection and ground icing wind tunnel tests.