Abstract
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.
Highlights
What is needed to demonstrate that AP of CFPN increases by 3.3% when compared with feature pyramid networks (FPN), compared with CFPN, AP of our smoothing complete feature pyramid networks (SCFPN) further increases by 4.8%
The α weighting function that is involved in complete intersection over union (CIoU) loss function provides the gradients about shape for the predicted boxes, prompting the predicted boxes to fit the size of the ground-truth boxes in shape more quickly
The intrinsic reason is that label smoothing restrains the overfitting of model and promotes the generalization ability effectively
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. As one of the most important fundamental materials in steel and iron industry, steel strips are extensively used in automobile manufacturing, locomotives, aerospace, precision instrumentation, etc. For thin and wide flat steel, surface defects are the greatest threat to the product quality. Even for occasional internal defects, morphological changes will arise on the surface with a large probability. Any quality problems suffering on steel surface would give rise to irretrievable economic and reputation losses to both the steel company and end use customer. To cope with the above issue, automated visual inspection (AVI)
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