UAV obstacle detection with bio-motivated computer vision

Unmanned aerial vehicles (UAVs) are becoming more and more common. They show excellent potential for multiple types of autonomous work, although they must achieve these tasks safely. For flight safety, it must be assured that the UA V will not endanger its surroundings during autonomous operations; it will avoid collision with any objects in its flight path. Camera-based computer vision and artificial neural networks have shown to be effective in many applications. However, biological vision systems and the brain areas responsible for visual processing may hold solutions capable of acquiring information effectively. Previous work has shown the usability of biologically motivated algorithms using vision systems of insects or even behavioral patterns to solve computer vision problems. We are proposing a novel system, which performs visual cue extraction with algorithms based on the structure and functionality of the retina and the visual cortex of the mammalian visual system. We are also developing a modular artificial neural network with a training dataset, which will perform autonomous obstacle recognition tasks using the data from the image processing algorithm.