Enhancing learning-based computer vision algorithms accuracy in sUAS using navigation wide-angle cameras
The new generation of sUAS (small Unmanned Aircraft Systems) aims to extend the range of scenarios in which sense-and-avoid functionality and autonomous operation can be used. Relying on navigation cameras, having a wide field of view can increase the coverage of the drone surroundings, allowing ideal fly path, optimal dynamic route planning and full situational awareness. The first part of this paper will discuss the trade-off space for camera hardware solution to improve vision performance. Severe constraints on size and weight, a situation common to all sUAS components, compete with low-light capabilities and pixel resolution. The second part will explore the benefits and impacts of specific wide-angle lens designs and of wide-angle images rectification (dewarping) on deep-learning methods. We show that distortion can be used to bring more information from the scene and how this extra information can increase the accuracy of learning-based computer vision algorithm. Finally, we present a study that aims at estimating the link between optical design criteria degradation (MTF) and neural network accuracy in the context of wide-angle lens, showing that higher MTF is not always linked to better results, thus helping to set better design targets for navigation lenses.
ImmerVision Inc. (Canada)
Xavier Dallaire has completed his studies at Laval University, Canada in 2017, graduating from a PhD degree in Physics - Optical Design. During his studies, he focused on topics related to miniaturization of wide-angle lenses, foveated imaging, and aberration correction using plenoptic imaging. Since 2017, Xavier has been working as an Optical Designer for Immervision in Montreal. At Immervision, he worked on the optical design of panomorph lenses in a wide range of applications, including consumer electronics, security, industrial, broadcast, medical and aerospace.
Also in the session