Cargando…

Tire Speckle Interference Bubble Defect Detection Based on Improved Faster RCNN-FPN

With the development of neural networks, object detection based on deep learning is developing rapidly, and its applications are gradually increasing. In the tire industry, detecting speckle interference bubble defects of tire crown has difficulties such as low image contrast, small object scale, an...

Descripción completa

Detalles Bibliográficos
Autores principales: Yang, Shihao, Jiao, Dongmei, Wang, Tongkun, He, Yan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9143011/
https://www.ncbi.nlm.nih.gov/pubmed/35632316
http://dx.doi.org/10.3390/s22103907
_version_ 1784715700357562368
author Yang, Shihao
Jiao, Dongmei
Wang, Tongkun
He, Yan
author_facet Yang, Shihao
Jiao, Dongmei
Wang, Tongkun
He, Yan
author_sort Yang, Shihao
collection PubMed
description With the development of neural networks, object detection based on deep learning is developing rapidly, and its applications are gradually increasing. In the tire industry, detecting speckle interference bubble defects of tire crown has difficulties such as low image contrast, small object scale, and large internal differences of defects, which affect the detection precision. To solve these problems, we propose a new feature pyramid network based on Faster RCNN-FPN. It can fuse features across levels and directions to improve small object detection and localization, and increase object detection precision. The method has proven its effectiveness through cross-validation experiments. On a tire crown bubble defect dataset, the mAP [0.5:0.95] increased by 2.08% and the AP0.5 increased by 2.4% over the original network. The results show that the improved network significantly improves detecting tire crown bubble defects.
format Online
Article
Text
id pubmed-9143011
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-91430112022-05-29 Tire Speckle Interference Bubble Defect Detection Based on Improved Faster RCNN-FPN Yang, Shihao Jiao, Dongmei Wang, Tongkun He, Yan Sensors (Basel) Article With the development of neural networks, object detection based on deep learning is developing rapidly, and its applications are gradually increasing. In the tire industry, detecting speckle interference bubble defects of tire crown has difficulties such as low image contrast, small object scale, and large internal differences of defects, which affect the detection precision. To solve these problems, we propose a new feature pyramid network based on Faster RCNN-FPN. It can fuse features across levels and directions to improve small object detection and localization, and increase object detection precision. The method has proven its effectiveness through cross-validation experiments. On a tire crown bubble defect dataset, the mAP [0.5:0.95] increased by 2.08% and the AP0.5 increased by 2.4% over the original network. The results show that the improved network significantly improves detecting tire crown bubble defects. MDPI 2022-05-21 /pmc/articles/PMC9143011/ /pubmed/35632316 http://dx.doi.org/10.3390/s22103907 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
Yang, Shihao
Jiao, Dongmei
Wang, Tongkun
He, Yan
Tire Speckle Interference Bubble Defect Detection Based on Improved Faster RCNN-FPN
title Tire Speckle Interference Bubble Defect Detection Based on Improved Faster RCNN-FPN
title_full Tire Speckle Interference Bubble Defect Detection Based on Improved Faster RCNN-FPN
title_fullStr Tire Speckle Interference Bubble Defect Detection Based on Improved Faster RCNN-FPN
title_full_unstemmed Tire Speckle Interference Bubble Defect Detection Based on Improved Faster RCNN-FPN
title_short Tire Speckle Interference Bubble Defect Detection Based on Improved Faster RCNN-FPN
title_sort tire speckle interference bubble defect detection based on improved faster rcnn-fpn
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9143011/
https://www.ncbi.nlm.nih.gov/pubmed/35632316
http://dx.doi.org/10.3390/s22103907
work_keys_str_mv AT yangshihao tirespeckleinterferencebubbledefectdetectionbasedonimprovedfasterrcnnfpn
AT jiaodongmei tirespeckleinterferencebubbledefectdetectionbasedonimprovedfasterrcnnfpn
AT wangtongkun tirespeckleinterferencebubbledefectdetectionbasedonimprovedfasterrcnnfpn
AT heyan tirespeckleinterferencebubbledefectdetectionbasedonimprovedfasterrcnnfpn