High Speed Marker Tracking for Flight Tests

Abstract

Flight testing is a mandatory process to ensure safety during normal operations and to evaluate an aircraft during its certification phase. As a test flight may be a high-risk activity that may result in loss of the aircraft or even loss of life, simulation models and real-time monitoring systems are crucial to access the risk and to increase situational awareness and safety. We propose a new detecting and tracking model based on CNN, that uses fiducial markers, called HSMT4FT. It is one of the main components of the Optical Trajectory System (SisTrO) which is responsible for detecting and tracking fiducial markers in external stores, in pylons, and in the wings of an aircraft during Flight Tests. HSMT4FT is a real-time processing model that is used to measure the trajectory in a store separation test and even to assess vibrations and wing deflections. Despite the fact that there are several libraries providing rule-based approaches for detecting predefined markers, this work contributes by developing and evaluating three convolutional neural network (CNN) models for detecting and localizing fiducial markers. We also compared classical methods for corner detection implemented in the OpenCV library and the neural network model executed in the OpenVINO environment. Both the execution time and the precision/accuracy of those methodologies were evaluated. One of the CNN models achieved the highest throughput, smaller RMSE, and highest F1 score among tested and benchmark models. The best model is fast enough to enable real-time applications in embedded systems and will be used for real detecting and tracking in real Flight Tests in the future.