Simulation Environment Implementation for Generation of Training Samples

Since their invention, drones have been used in many areas, both civil and military. Among these uses, some of the more important ones are reconnaissance, patrolling and object recognition. In most of the applications, objects in images are detected using neural networks. To carry out this process, datasets with a lot of training samples should be collected. Unlabeled data takes a lot of time and human resources to process. Carrying out real drone measurements is a long process and is affected by many external factors, such as weather conditions. A simulation environment can provide a solution to this problem, where different drone measurements can be simulated in varied environments. One of the advantages of the simulation environment is that the objects on the acquired images can be automatically labeled, which is a very time-consuming process in the case of manually labeled images. An additional advantage is that it can perform measurements on diverse terrains with realistic objects with the help of specified configurations, thus generating a large and diverse set of training samples with minimal human intervention. The paper contains information on how to generate different terrains, navigate them with a virtual drone, taking pictures and labeling them at the same time. Development possibilities include dynamically changing the appearance of objects, as well as introducing moving objects into the simulation. It has been proven that it is possible to generate a diverse training set with the help of the proposed simulation.