Pulse-coupled neural network performance for real-time identification of vegetation during forced landing

Abstract

Safety concerns in the operation of autonomous aerial systems require safe-landing protocols be followed during situations where the mission should be aborted due to mechanical or other failure. This article presents a pulse-coupled neural network (pcnn) to assist in the vegetation classification in a vision-based landing site detection system for an unmanned aircraft. We propose a heterogeneous computing architecture and an Opencl implementation of a pcnn feature generator. Its performance is compared across Opencl kernels designed for cpu, gpu, and FPGA platforms. This comparison examines the compute times required for network convergence under a variety of images to determine the plausibility for real-time feature detection.