Track Fastener Detection Based on Improved YOLOv4-Tiny Network
Author:
Affiliation:
1. Beijing Jiaotong University,School of Electronic and Information Engineering,Beijing,China
2. Beijing Jiaotong University,State Key Laboratory of Rail Traffic Control and Safety,Beijing,China
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9823394/9823964/09824503.pdf?arnumber=9824503
Reference15 articles.
1. Online defect recognition of narrow overlap weld based on two-stage recognition model combining continuous wavelet transform and convolutional neural network
2. Railway track fastener defect detection based on image processing and deep learning techniques: A comparative study
3. Automatic Defect Detection of Fasteners on the Catenary Support Device Using Deep Convolutional Neural Network[J];chen;IEEE Transactions on Instrumentation and Measurement,2017
4. YOLOv4: Optimal Speed and Accuracy of Object Detection[J];bochkovskiy,2020
5. Mish: A Self Regularized Non-Monotonic Neural Activation Function[J];misra,2019
Cited by 1 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. High Speed Railway Fastener Defect Detection by Using Improved YoLoX-Nano Model;Sensors;2022-11-01
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