A Multi-Step CNN-Based Estimation of Aircraft Landing Gear Angles

Sensors

Zhitong Lai¹  Fuyang Li¹  Zhiguo Wu¹  Jingyu Li¹  Botong Zhao¹  Chen Min² 

[1] Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China;School of Computer Science, Peking University, Beijing 100091, China;

关键词: monocular detection;    landing gear angle;    CAD model;    multi-step;    CNN-based;   

DOI  :  10.3390/s21248440

来源: DOAJ

【 摘 要 】

This paper presents a method for measuring aircraft landing gear angles based on a monocular camera and the CAD aircraft model. Condition monitoring of the aircraft landing gear is a prerequisite for the safe landing of the aircraft. Traditional manual observation has an intense subjectivity. In recent years, target detection models dependent on deep learning and pose estimation methods relying on a single RGB image have made significant progress. Based on these advanced algorithms, this paper proposes a method for measuring the actual angles of landing gears in two-dimensional images. A single RGB image of an aircraft is inputted to the target detection module to obtain the key points of landing gears. The vector field network votes the key points of the fuselage after extraction and scale normalization of the pixels inside the aircraft prediction box. Knowing the pixel position of the key points and the constraints on the aircraft, the angle between the landing gear and fuselage plane can be calculated even without depth information. The vector field loss function is improved based on the distance between pixels and key points, and synthetic datasets of aircraft with different angle landing gears are created to verify the validity of the proposed algorithm. The experimental results show that the mean error of the proposed algorithm for the landing gears is less than 5 degrees on the light-varying dataset.

【 授权许可】

Unknown