Track Fastener Detection Based on Improved YOLOv4-Tiny Network-Reference-Cited by-同舟云学术

Track Fastener Detection Based on Improved YOLOv4-Tiny Network

Published:2022-05-13 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE 5th International Conference on Electronics Technology (ICET)
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Author:

Xiao Tian¹,Xu Tianhua²

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

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1. Automated detection of railway defective fasteners based on YOLOv8-FAM and synthetic data using style transfer;Automation in Construction;2024-06

2. PB-YOLO for Railway Fastener Detection: A Higher Accuracy and Lightweight Model With PConv and BiFPN;2023 Global Reliability and Prognostics and Health Management Conference (PHM-Hangzhou);2023-10-12

3. High Speed Railway Fastener Defect Detection by Using Improved YoLoX-Nano Model;Sensors;2022-11-01