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High Speed Railway Fastener Defect Detection by Using Improved YoLoX-Nano Model

Rails play a vital role in the bearing and guidance of high-speed trains, and the normal condition of rail components is the guarantee of the operation and maintenance safety. Fasteners are critical components for fixing the rails, so it is particularly important to detect whether they are in a norm...

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Autores principales: Hu, Jun, Qiao, Peng, Lv, Haohao, Yang, Liang, Ouyang, Aiguo, He, Yong, Liu, Yande
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9654688/
https://www.ncbi.nlm.nih.gov/pubmed/36366094
http://dx.doi.org/10.3390/s22218399
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author Hu, Jun
Qiao, Peng
Lv, Haohao
Yang, Liang
Ouyang, Aiguo
He, Yong
Liu, Yande
author_facet Hu, Jun
Qiao, Peng
Lv, Haohao
Yang, Liang
Ouyang, Aiguo
He, Yong
Liu, Yande
author_sort Hu, Jun
collection PubMed
description Rails play a vital role in the bearing and guidance of high-speed trains, and the normal condition of rail components is the guarantee of the operation and maintenance safety. Fasteners are critical components for fixing the rails, so it is particularly important to detect whether they are in a normal state or not. The current rail-fastener detection models have some drawbacks, including poor generalization ability, large model volume and low detection efficiency. In view of this, an improved YoLoX-Nano rail-fastener-defect-detection method is proposed in this paper. The CA attention mechanism is added to the three output feature maps of CSPDarknet and the enhanced feature extraction part of the Path Aggregation Feature Pyramid Network (PAFPN); the Adaptively Spatial Feature Fusion (ASFF) is added after the PAFPN output feature map, which enables the semantic information of the high-level features and the fine-grained features of the bottom layer to be further enhanced. The improved YoLoX-Nano model has improved the AP value by 27.42% on fractured fasteners, 15.88% on displacement fasteners and 12.96% on normal fasteners. Moreover, the mAP value is improved by 18.75%, and it is 14.75% higher than the two-stage model Faster-RCNN on mAP. In addition, compared with YoLov7-tiny, the improved YoLoX-Nano model achieves 13.56% improvement on mAP. Although the improved model increases a certain amount of calculation, the detection speed of the improved model has been increased by 30.54 fps and by 32.33 fps when compared with that of the Single-Shot Multi-Box Detector (SSD) model and the You Only Look Once v3 (YoLov3) model, reaching 54.35 fps. The improved YoLoX-Nano model enables accurate and rapid identification of the defects of rail fasteners, which can meet the needs of real-time detection. Furthermore, it has advantages in lightweight deployment of terminals for rail-fastener detection, thus providing some reference for image recognition and detection in other fields.
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spelling pubmed-96546882022-11-15 High Speed Railway Fastener Defect Detection by Using Improved YoLoX-Nano Model Hu, Jun Qiao, Peng Lv, Haohao Yang, Liang Ouyang, Aiguo He, Yong Liu, Yande Sensors (Basel) Article Rails play a vital role in the bearing and guidance of high-speed trains, and the normal condition of rail components is the guarantee of the operation and maintenance safety. Fasteners are critical components for fixing the rails, so it is particularly important to detect whether they are in a normal state or not. The current rail-fastener detection models have some drawbacks, including poor generalization ability, large model volume and low detection efficiency. In view of this, an improved YoLoX-Nano rail-fastener-defect-detection method is proposed in this paper. The CA attention mechanism is added to the three output feature maps of CSPDarknet and the enhanced feature extraction part of the Path Aggregation Feature Pyramid Network (PAFPN); the Adaptively Spatial Feature Fusion (ASFF) is added after the PAFPN output feature map, which enables the semantic information of the high-level features and the fine-grained features of the bottom layer to be further enhanced. The improved YoLoX-Nano model has improved the AP value by 27.42% on fractured fasteners, 15.88% on displacement fasteners and 12.96% on normal fasteners. Moreover, the mAP value is improved by 18.75%, and it is 14.75% higher than the two-stage model Faster-RCNN on mAP. In addition, compared with YoLov7-tiny, the improved YoLoX-Nano model achieves 13.56% improvement on mAP. Although the improved model increases a certain amount of calculation, the detection speed of the improved model has been increased by 30.54 fps and by 32.33 fps when compared with that of the Single-Shot Multi-Box Detector (SSD) model and the You Only Look Once v3 (YoLov3) model, reaching 54.35 fps. The improved YoLoX-Nano model enables accurate and rapid identification of the defects of rail fasteners, which can meet the needs of real-time detection. Furthermore, it has advantages in lightweight deployment of terminals for rail-fastener detection, thus providing some reference for image recognition and detection in other fields. MDPI 2022-11-01 /pmc/articles/PMC9654688/ /pubmed/36366094 http://dx.doi.org/10.3390/s22218399 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
Hu, Jun
Qiao, Peng
Lv, Haohao
Yang, Liang
Ouyang, Aiguo
He, Yong
Liu, Yande
High Speed Railway Fastener Defect Detection by Using Improved YoLoX-Nano Model
title High Speed Railway Fastener Defect Detection by Using Improved YoLoX-Nano Model
title_full High Speed Railway Fastener Defect Detection by Using Improved YoLoX-Nano Model
title_fullStr High Speed Railway Fastener Defect Detection by Using Improved YoLoX-Nano Model
title_full_unstemmed High Speed Railway Fastener Defect Detection by Using Improved YoLoX-Nano Model
title_short High Speed Railway Fastener Defect Detection by Using Improved YoLoX-Nano Model
title_sort high speed railway fastener defect detection by using improved yolox-nano model
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9654688/
https://www.ncbi.nlm.nih.gov/pubmed/36366094
http://dx.doi.org/10.3390/s22218399
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