Corpus ID: 202540939

Recognition Of Surface Defects On Steel Sheet Using Transfer Learning

@article{Fu2019RecognitionOS,
  title={Recognition Of Surface Defects On Steel Sheet Using Transfer Learning},
  author={Jingwen Fu and Xiaoyan Zhu and Yingbin Li},
  journal={ArXiv},
  year={2019},
  volume={abs/1909.03258}
}
Automatic defect recognition is one of the research hotspots in steel production, but most of the current methods mainly extract features manually and use machine learning classifiers to recognize defects, which cannot tackle the situation, where there are few data available to train and confine to a certain scene. Therefore, in this paper, a new approach is proposed which consists of part of pretrained VGG16 as a feature extractor and a new CNN neural network as a classifier to recognize the… Expand
1 Citations
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References

SHOWING 1-10 OF 36 REFERENCES
Classification of surface defects on steel sheet using convolutional neural networks
A convolutional neural network (CNN) is proposed to learn multiple useful feature representations for a classification from low level (raw pixels) to high level (object). Convolutional kernels areExpand
A semi-supervised convolutional neural network-based method for steel surface defect recognition
TLDR
A semi-supervised learning method using the convolutional neural network (CNN) is proposed for steel surface defect recognition, which requires fewer labeled samples, and the unlabeled data can be used to help training. Expand
Vision-based defect detection of scale-covered steel billet surfaces
TLDR
The experimental results conducted on billet surface images obtained from actual steel production lines show that the proposed algorithm is effective for defect detection of scale-covered steel billet surfaces. Expand
Deep Convolutional Neural Networks with transfer learning for computer vision-based data-driven pavement distress detection
Abstract Automated pavement distress detection and classification has remained one of the high-priority research areas for transportation agencies. In this paper, we employed a Deep ConvolutionalExpand
A noise robust method based on completed local binary patterns for hot-rolled steel strip surface defects
TLDR
Experimental results demonstrate that the proposed approach presents the performance of defect recognition under the influence of the feature variations of the intra-class changes, the illumination and grayscale changes, and even in the toughest situation with additive Gaussian noise, the AECLBP can still achieve the moderate recognition accuracy. Expand
Inception Dual Network for steel strip defect detection
TLDR
A new neural network called Inception Dual Network (IDN) is proposed to solve steel defect detection problem and has achieved the best performance among some state-of-the-art structure with similar number of parameters while keeping real-time. Expand
Automatic detection of cracks in raw steel block using Gabor filter optimized by univariate dynamic encoding algorithm for searches (uDEAS)
Recently, there has been an increase in the demand for quality control in the steel making industry. This paper proposes a vision-based method for detection of defects in the surfaces ofExpand
Online Detection Technique of 3D Defects for Steel Strips Based on Photometric Stereo
  • L. Wang, Ke Xu, Peng Zhou
  • Materials Science
  • 2016 Eighth International Conference on Measuring Technology and Mechatronics Automation (ICMTMA)
  • 2016
2D Detection with gray level images is popularly employed in current surface inspection systems of steel strips, which lead to high false detection rate because of interference of pseudo-defects,Expand
A multi-scale convolutional neural network for autonomous anomaly detection and classification in a laser powder bed fusion additive manufacturing process
Abstract In-situ detection of processing defects is a critical challenge for Laser Powder Bed Fusion Additive Manufacturing. Many of these defects are related to interactions between the recoaterExpand
Vision-Based Sensor for Early Detection of Periodical Defects in Web Materials
TLDR
A vision-based sensor for the early detection of periodical defects in hot steel strips that can be adapted to be used in the inspection of any web material, even when the input data are very noisy. Expand
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