Reality Analagous Synthetic Dataset Generation with Daylight Variance for Deep Learning Classification

For the implementation of Autonomously navigating Unmanned Air Vehicles (UAV) in the real world, it must be shown that safe navigation is possible in all real world scenarios. In the case of UAVs powered by Deep Learning algorithms, this is a difficult task to achieve, as the weak point of any trained network is the reduction in predictive capacity when presented with unfamiliar input data. It is possible to train for more use cases, however more data is required for this, requiring time and manpower to acquire. In this work, a potential solution to the manpower issues of exponentially scaling dataset size and complexity is presented, through the generation of artificial image datasets that are based off of a 3D scanned recreation of a physical space and populated with 3D scanned objects of a specific class. This simulation is then used to generate image samples that iterates temporally resulting in a slice-able dataset that contains time varied components of the same class