  In this study, we introduce EdgeSegNet, a compact deep convolutional neural
network for the task of semantic segmentation. A human-machine collaborative
design strategy is leveraged to create EdgeSegNet, where principled network
design prototyping is coupled with machine-driven design exploration to create
networks with customized module-level macroarchitecture and microarchitecture
designs tailored for the task. Experimental results showed that EdgeSegNet can
achieve semantic segmentation accuracy comparable with much larger and
computationally complex networks (>20x} smaller model size than RefineNet) as
well as achieving an inference speed of ~38.5 FPS on an NVidia Jetson AGX
Xavier. As such, the proposed EdgeSegNet is well-suited for low-power edge
scenarios.
