Performance Comparison of Deep Learning Networks for Runway Recognition in Small Edge Computing Environment
Hyunjee Ryu, Hongju Lee, Kyunam Kim · 2023
The purpose of this work is to investigate and compare three popular segmentation networks for runway recognition in a small edge computing environment that has the potential to become a part of avionics in the near future. For the networks, we examine YolactEdge, STDC-seg, and DDRNet, and for the small edge computing environment, we choose NVidia Jetson Orin AGX and Xavier NX. In order to verify their real-time performance as an on-board system, we first tested them on three different edge computers. We also conducted multiple flight tests with an actual aircraft and constructed a runway image dataset that was used for the training and evaluation of the networks. According to our result, YolactEdge, which is an instance segmentation model, showed a good performance, but it was difficult to run in real-time on our small computing environment. STDC-seg and DDRNet, which are semantic segmentation models, presented over 0.95 mIoU with approximately 0.1 seconds of inference time. In particular, DDRNet-23-slim, which is the smallest model of DDRNet, showed the highest mIoU and the shortest inference time among the networks we tested in this work.