Cargando…
An Online Rail Track Fastener Classification System Based on YOLO Models
In order to save manpower on rail track inspection, computer vision-based methodologies are developed. We propose utilizing the YOLOv4-Tiny neural network to identify track defects in real time. There are ten defects covering fasteners, rail surfaces, and sleepers from the upward and six defects abo...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9783312/ https://www.ncbi.nlm.nih.gov/pubmed/36560339 http://dx.doi.org/10.3390/s22249970 |
_version_ | 1784857548432605184 |
---|---|
author | Hsieh, Chen-Chiung Hsu, Ti-Yun Huang, Wei-Hsin |
author_facet | Hsieh, Chen-Chiung Hsu, Ti-Yun Huang, Wei-Hsin |
author_sort | Hsieh, Chen-Chiung |
collection | PubMed |
description | In order to save manpower on rail track inspection, computer vision-based methodologies are developed. We propose utilizing the YOLOv4-Tiny neural network to identify track defects in real time. There are ten defects covering fasteners, rail surfaces, and sleepers from the upward and six defects about the rail waist from the sideward. The proposed real-time inspection system includes a high-performance notebook, two sports cameras, and three parallel processes. The hardware is mounted on a flat cart running at 30 km/h. The inspection results about the abnormal track components could be queried by defective type, time, and the rail hectometer stake. In the experiments, data augmentation by a Cycle Generative Adversarial Network (GAN) is used to increase the dataset. The number of images is 3800 on the upward and 967 on the sideward. Five object detection neural network models—YOLOv4, YOLOv4-Tiny, YOLOX-Tiny, SSD512, and SSD300—were tested. The YOLOv4-Tiny model with 150 FPS is selected as the recognition kernel, as it achieved 91.7%, 92%, and 91% for the mAP, precision, and recall of the defective track components from the upward, respectively. The mAP, precision, and recall of the defective track components from the sideward are 99.16%, 96%, and 94%, respectively. |
format | Online Article Text |
id | pubmed-9783312 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-97833122022-12-24 An Online Rail Track Fastener Classification System Based on YOLO Models Hsieh, Chen-Chiung Hsu, Ti-Yun Huang, Wei-Hsin Sensors (Basel) Article In order to save manpower on rail track inspection, computer vision-based methodologies are developed. We propose utilizing the YOLOv4-Tiny neural network to identify track defects in real time. There are ten defects covering fasteners, rail surfaces, and sleepers from the upward and six defects about the rail waist from the sideward. The proposed real-time inspection system includes a high-performance notebook, two sports cameras, and three parallel processes. The hardware is mounted on a flat cart running at 30 km/h. The inspection results about the abnormal track components could be queried by defective type, time, and the rail hectometer stake. In the experiments, data augmentation by a Cycle Generative Adversarial Network (GAN) is used to increase the dataset. The number of images is 3800 on the upward and 967 on the sideward. Five object detection neural network models—YOLOv4, YOLOv4-Tiny, YOLOX-Tiny, SSD512, and SSD300—were tested. The YOLOv4-Tiny model with 150 FPS is selected as the recognition kernel, as it achieved 91.7%, 92%, and 91% for the mAP, precision, and recall of the defective track components from the upward, respectively. The mAP, precision, and recall of the defective track components from the sideward are 99.16%, 96%, and 94%, respectively. MDPI 2022-12-17 /pmc/articles/PMC9783312/ /pubmed/36560339 http://dx.doi.org/10.3390/s22249970 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Hsieh, Chen-Chiung Hsu, Ti-Yun Huang, Wei-Hsin An Online Rail Track Fastener Classification System Based on YOLO Models |
title | An Online Rail Track Fastener Classification System Based on YOLO Models |
title_full | An Online Rail Track Fastener Classification System Based on YOLO Models |
title_fullStr | An Online Rail Track Fastener Classification System Based on YOLO Models |
title_full_unstemmed | An Online Rail Track Fastener Classification System Based on YOLO Models |
title_short | An Online Rail Track Fastener Classification System Based on YOLO Models |
title_sort | online rail track fastener classification system based on yolo models |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9783312/ https://www.ncbi.nlm.nih.gov/pubmed/36560339 http://dx.doi.org/10.3390/s22249970 |
work_keys_str_mv | AT hsiehchenchiung anonlinerailtrackfastenerclassificationsystembasedonyolomodels AT hsutiyun anonlinerailtrackfastenerclassificationsystembasedonyolomodels AT huangweihsin anonlinerailtrackfastenerclassificationsystembasedonyolomodels AT hsiehchenchiung onlinerailtrackfastenerclassificationsystembasedonyolomodels AT hsutiyun onlinerailtrackfastenerclassificationsystembasedonyolomodels AT huangweihsin onlinerailtrackfastenerclassificationsystembasedonyolomodels |