Towards Real-Time Segmentation on the Edge

Author:

Li Yanyu,Yang Changdi,Zhao Pu,Yuan Geng,Niu Wei,Guan Jiexiong,Tang Hao,Qin Minghai,Jin Qing,Ren Bin,Lin Xue,Wang Yanzhi

Abstract

The research in real-time segmentation mainly focuses on desktop GPUs. However, autonomous driving and many other applications rely on real-time segmentation on the edge, and current arts are far from the goal. In addition, recent advances in vision transformers also inspire us to re-design the network architecture for dense prediction task. In this work, we propose to combine the self attention block with lightweight convolutions to form new building blocks, and employ latency constraints to search an efficient sub-network. We train an MLP latency model based on generated architecture configurations and their latency measured on mobile devices, so that we can predict the latency of subnets during search phase. To the best of our knowledge, we are the first to achieve over 74% mIoU on Cityscapes with semi-real-time inference (over 15 FPS) on mobile GPU from an off-the-shelf phone.

Publisher

Association for the Advancement of Artificial Intelligence (AAAI)

Subject

General Medicine

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Patronus: Countering Model Poisoning Attacks in Edge Distributed DNN Training;2024 IEEE Wireless Communications and Networking Conference (WCNC);2024-04-21

2. Deep learning-based real-time semantic segmentation:a survey;Journal of Image and Graphics;2024

3. Pruning Parameterization with Bi-level Optimization for Efficient Semantic Segmentation on the Edge;2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR);2023-06

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