# EdgeSegNet: A Compact Network for Semantic Segmentation

arXiv: https://arxiv.org/abs/1905.04222 · HTML: https://mlsanity.com/paper/1905.04222/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 May 2019.

## Built on by (most cited first, 4 of 4)

- [eWaSR -- an embedded-compute-ready maritime obstacle detection network](https://mlsanity.com/paper/2304.11249/) (2023)
- [AttendSeg: A Tiny Attention Condenser Neural Network for Semantic Segmentation on the Edge](https://mlsanity.com/paper/2104.14623/) (2021)
- [L^3U-net: Low-Latency Lightweight U-net Based Image Segmentation Model for Parallel CNN Processors](https://mlsanity.com/paper/2203.16528/) (2022)
- [Are We Ready for Real-Time LiDAR Semantic Segmentation in Autonomous Driving?](https://mlsanity.com/paper/2410.08365/) (2024)

## Abstract (arXiv)

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.