The Accelerated Inference of a Novel Optimized YOLOv5-LITE on Low-Power Devices for Railway Track Damage Detection

Author:

Dang Chao1,Wang Zaixing1ORCID,He Yonghuan1ORCID,Wang Linchang1,Cai Yi1,Shi Huiji1,Jiang Jiachi1

Affiliation:

1. School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou, China

Funder

Gansu Province science and technology Department plan project

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Cited by 4 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

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2. An Improved YOLOv5s Model for Building Detection;Electronics;2024-06-04

3. IoT-SafeRails: Revolutionizing Railway Collision Avoidance Technology;2024 International Conference on Inventive Computation Technologies (ICICT);2024-04-24

4. GS-YOLO: A Lightweight SAR Ship Detection Model Based on Enhanced GhostNetV2 and SE Attention Mechanism;IEEE Access;2024

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