# Efficient Semantic Segmentation on Edge Devices

arXiv: https://arxiv.org/abs/2212.13691 · HTML: https://mlsanity.com/paper/2212.13691/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 81 references that are in the map.
- First posted on arXiv 28 Dec 2022.

## Builds on (most cited first, 18 of 18)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation](https://mlsanity.com/paper/1808.00897/) (2018)
- [BiSeNet V2: Bilateral Network with Guided Aggregation for Real-time Semantic Segmentation](https://mlsanity.com/paper/2004.02147/) (2020)
- [FloodNet: A High Resolution Aerial Imagery Dataset for Post Flood Scene Understanding](https://mlsanity.com/paper/2012.02951/) (2020)
- [Knowledge Adaptation for Efficient Semantic Segmentation](https://mlsanity.com/paper/1903.04688/) (2019)
- [Deep Dual-resolution Networks for Real-time and Accurate Semantic Segmentation of Road Scenes](https://mlsanity.com/paper/2101.06085/) (2021)
- [Efficient RGB-D Semantic Segmentation for Indoor Scene Analysis](https://mlsanity.com/paper/2011.06961/) (2020)
- [HarDNet: A Low Memory Traffic Network](https://mlsanity.com/paper/1909.00948/) (2019)
- [A Survey on Deep Learning-based Architectures for Semantic Segmentation on 2D images](https://mlsanity.com/paper/1912.10230/) (2019)
- [Attention-based Dual Supervised Decoder for RGBD Semantic Segmentation](https://mlsanity.com/paper/2201.01427/) (2022)
- [Computer Vision Based Parking Optimization System](https://mlsanity.com/paper/2201.00095/) (2022)
- [MailLeak: Obfuscation-Robust Character Extraction Using Transfer Learning](https://mlsanity.com/paper/2012.11775/) (2020)
- [Benchmarking Machine Learning: How Fast Can Your Algorithms Go?](https://mlsanity.com/paper/2101.03219/) (2021)
- [Chatbot for fitness management using IBM Watson](https://mlsanity.com/paper/2112.15167/) (2021)
- [Machine Learning and Artificial Intelligence in Next-Generation Wireless Network](https://mlsanity.com/paper/2202.01690/) (2021)
- [A Secured Protocol for IoT Networks](https://mlsanity.com/paper/2012.11072/) (2020)

## Built on by (most cited first, 1 of 1)

- [Data Classification With Multiprocessing](https://mlsanity.com/paper/2312.15152/) (2023)

## Abstract (arXiv)

Semantic segmentation works on the computer vision algorithm for assigning each pixel of an image into a class. The task of semantic segmentation should be performed with both accuracy and efficiency. Most of the existing deep FCNs yield to heavy computations and these networks are very power hungry, unsuitable for real-time applications on portable devices. This project analyzes current semantic segmentation models to explore the feasibility of applying these models for emergency response during catastrophic events. We compare the performance of real-time semantic segmentation models with non-real-time counterparts constrained by aerial images under oppositional settings. Furthermore, we train several models on the Flood-Net dataset, containing UAV images captured after Hurricane Harvey, and benchmark their execution on special classes such as flooded buildings vs. non-flooded buildings or flooded roads vs. non-flooded roads. In this project, we developed a real-time UNet based model and deployed that network on Jetson AGX Xavier module.