Efficient Deep Vision for Aerial Visual Understanding

Unmanned Aerial Vehicles (UAVs) are becoming a growing necessity for a broad range of applications, such as emergency response, monitoring critical infrastructures, and disaster management. UAVs, due to their affordability and camera capabilities, have become a common mobile camera platform for these kinds of applications. Thus, visual perception by utilizing Convolutional Neural Networks (CNNs) and Deep Learning is a key necessity for UAV-based applications. The remarkable performance of deep neural networks (DNNs) for vision tasks comes at a cost of high computational demands where the problem is amplified in drone-based applications due to limited energy resource. To address these drawbacks, this chapter highlights some of the key techniques of making deep vision more efficient for such resource-constrained applications. The techniques include but are not limited to data selection and reduction, efficient neural network design, and hardware-oriented model optimization. Results on different use cases show that such techniques can provide improvements either when applied as standalone or in a combined manner.