Development of a Synthetic Dataset Using Aerial Navigation to Validate a Texture Classification Model
Juan Manuel Fortuna-Cervantes, M. T. Ramírez-Torres, M. Mejía-Carlos, José Martínez-Carranza · 2024
An essential part of aerial navigation is to detect and locate targets of interest. This research chapter focuses on object classification, where the important feature is texture. We present a classification model that uses transfer learning and wavelet-based features as an additional feature extraction method. This model is trained with the Describable Texture Database (DTD), a 53% accuracy is obtained. We developed a virtual world in the Gazebo simulator to validate the results. The virtual world allowed us to create a new Synthetic Aerial Dataset of Textured Objects (SADTO) and integrate the Parrot AR.Drone model with its flight features. ROS Kinetic is used to send information from the on-board camera of the drone (virtual environment) to the PC. The images from the environment show a generalization of the knowledge for some classes of the database.