Smoothing Complete Feature Pyramid Networks for Roll Mark Detection of Steel Strips

Steel strip acts as a fundamental material for the steel industry. Surface defects threaten the steel quality and cause substantial economic and reputation losses. Roll marks, always occurring periodically in a large area, are put on the top of the list of the most serious defects by steel mills. Es...

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Autores principales:	Qiwu Luo, Weiqiang Jiang, Jiaojiao Su, Jiaqiu Ai, Chunhua Yang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	surface defect detection roll marks hot-rolled steel feature pyramid networks (FPN) Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/12af86a8f1bb42089597c5ce70089c8a
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id	oai:doaj.org-article:12af86a8f1bb42089597c5ce70089c8a
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spelling	oai:doaj.org-article:12af86a8f1bb42089597c5ce70089c8a2021-11-11T19:13:40ZSmoothing Complete Feature Pyramid Networks for Roll Mark Detection of Steel Strips10.3390/s212172641424-8220https://doaj.org/article/12af86a8f1bb42089597c5ce70089c8a2021-10-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/21/7264https://doaj.org/toc/1424-8220Steel strip acts as a fundamental material for the steel industry. Surface defects threaten the steel quality and cause substantial economic and reputation losses. Roll marks, always occurring periodically in a large area, are put on the top of the list of the most serious defects by steel mills. Essentially, the online roll mark detection is a tiny target inspection task in high-resolution images captured under harsh environment. In this paper, a novel method—namely, Smoothing Complete Feature Pyramid Networks (SCFPN)—is proposed for the above focused task. In particular, the concept of complete intersection over union (CIoU) is applied in feature pyramid networks to obtain faster fitting speed and higher prediction accuracy by suppressing the vanishing gradient in training process. Furthermore, label smoothing is employed to promote the generalization ability of model. In view of lack of public surface image database of steel strips, a raw defect database of hot-rolled steel strip surface, CSU_STEEL, is opened for the first time. Experiments on two public databases (DeepPCB and NEU) and one fresh texture database (CSU_STEEL) indicate that our SCFPN yields more competitive results than several prestigious networks—including Faster R-CNN, SSD, YOLOv3, YOLOv4, FPN, DIN, DDN, and CFPN.Qiwu LuoWeiqiang JiangJiaojiao SuJiaqiu AiChunhua YangMDPI AGarticlesurface defect detectionroll markshot-rolled steelfeature pyramid networks (FPN)Chemical technologyTP1-1185ENSensors, Vol 21, Iss 7264, p 7264 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	surface defect detection roll marks hot-rolled steel feature pyramid networks (FPN) Chemical technology TP1-1185
spellingShingle	surface defect detection roll marks hot-rolled steel feature pyramid networks (FPN) Chemical technology TP1-1185 Qiwu Luo Weiqiang Jiang Jiaojiao Su Jiaqiu Ai Chunhua Yang Smoothing Complete Feature Pyramid Networks for Roll Mark Detection of Steel Strips
description	Steel strip acts as a fundamental material for the steel industry. Surface defects threaten the steel quality and cause substantial economic and reputation losses. Roll marks, always occurring periodically in a large area, are put on the top of the list of the most serious defects by steel mills. Essentially, the online roll mark detection is a tiny target inspection task in high-resolution images captured under harsh environment. In this paper, a novel method—namely, Smoothing Complete Feature Pyramid Networks (SCFPN)—is proposed for the above focused task. In particular, the concept of complete intersection over union (CIoU) is applied in feature pyramid networks to obtain faster fitting speed and higher prediction accuracy by suppressing the vanishing gradient in training process. Furthermore, label smoothing is employed to promote the generalization ability of model. In view of lack of public surface image database of steel strips, a raw defect database of hot-rolled steel strip surface, CSU_STEEL, is opened for the first time. Experiments on two public databases (DeepPCB and NEU) and one fresh texture database (CSU_STEEL) indicate that our SCFPN yields more competitive results than several prestigious networks—including Faster R-CNN, SSD, YOLOv3, YOLOv4, FPN, DIN, DDN, and CFPN.
format	article
author	Qiwu Luo Weiqiang Jiang Jiaojiao Su Jiaqiu Ai Chunhua Yang
author_facet	Qiwu Luo Weiqiang Jiang Jiaojiao Su Jiaqiu Ai Chunhua Yang
author_sort	Qiwu Luo
title	Smoothing Complete Feature Pyramid Networks for Roll Mark Detection of Steel Strips
title_short	Smoothing Complete Feature Pyramid Networks for Roll Mark Detection of Steel Strips
title_full	Smoothing Complete Feature Pyramid Networks for Roll Mark Detection of Steel Strips
title_fullStr	Smoothing Complete Feature Pyramid Networks for Roll Mark Detection of Steel Strips
title_full_unstemmed	Smoothing Complete Feature Pyramid Networks for Roll Mark Detection of Steel Strips
title_sort	smoothing complete feature pyramid networks for roll mark detection of steel strips
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/12af86a8f1bb42089597c5ce70089c8a
work_keys_str_mv	AT qiwuluo smoothingcompletefeaturepyramidnetworksforrollmarkdetectionofsteelstrips AT weiqiangjiang smoothingcompletefeaturepyramidnetworksforrollmarkdetectionofsteelstrips AT jiaojiaosu smoothingcompletefeaturepyramidnetworksforrollmarkdetectionofsteelstrips AT jiaqiuai smoothingcompletefeaturepyramidnetworksforrollmarkdetectionofsteelstrips AT chunhuayang smoothingcompletefeaturepyramidnetworksforrollmarkdetectionofsteelstrips
_version_	1718431568879419392

Smoothing Complete Feature Pyramid Networks for Roll Mark Detection of Steel Strips

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