A Method for Steel Surface Defect Recognition Based on Deep Learning and Receptive Field Block

2021 ◽  
Author(s):  
Jinyuan Gan ◽  
Chaobing Huang
Keyword(s):  
Deep Learning ◽  
Receptive Field ◽  
Steel Surface ◽  
Surface Defect ◽  
Defect Recognition

scholarly journals Strip Steel Surface Defects Recognition Based on SOCP Optimized Multiple Kernel RVM

2018 ◽  
Vol 2018 ◽  
pp. 1-8
Author(s):  
Hou Jingzhong ◽  
Xia Kewen ◽  
Yang Fan ◽  
Zu Baokai
Keyword(s):  
Steel Surface ◽  
Surface Defect ◽  
Surface Defects ◽  
Recognition Rate ◽  
Strip Surface ◽  
Strip Steel ◽  
Practical Applications ◽  
Multiple Kernel ◽  
Low Efficiency ◽  
Defect Recognition

Strip steel surface defect recognition is a pattern recognition problem with wide applications. Previous works on strip surface defect recognition mainly focus on feature selection and dimension reduction. There are also approaches on real-time systems that mainly exploit the autocorrection within some given picture. However, the instances cannot be used in practical applications because of a bad recognition rate and low efficiency. In this paper, we study the intelligent algorithm of strip steel surface defect recognition, where the goal is to improve the accuracy and save running time. This problem is very important in various applications, especially the process testing of steel manufacturing. We propose an approach called the second-order cone programming (SOCP) optimized multiple kernel relevance vector machine (MKRVM), which can recognize strip surface defects much better than other methods. The method includes the model parameter estimation, training, and optimization of the model based on SOCP and the classification test. We compare our approach with existing methods on strip surface defect recognition. The results demonstrate that our proposed approach can improve the recognition accuracy and reduce the time costs of the strip surface defect.

scholarly journals A New Steel Defect Detection Algorithm Based on Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Weidong Zhao ◽  
Feng Chen ◽  
Hancheng Huang ◽  
Dan Li ◽  
Wei Cheng
Keyword(s):  
Deep Learning ◽  
Target Detection ◽  
Defect Detection ◽  
Steel Surface ◽  
Surface Defect ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Average Precision ◽  
Original Algorithm ◽  
Surface Defect Detection

In recent years, more and more scholars devoted themselves to the research of the target detection algorithm due to the continuous development of deep learning. Among them, the detection and recognition of small and complex targets are still a problem to be solved. The authors of this article have understood the shortcomings of the deep learning detection algorithm in detecting small and complex defect targets and would like to share a new improved target detection algorithm in steel surface defect detection. The steel surface defects will affect the quality of steel seriously. We find that most of the current detection algorithms for NEU-DET dataset detection accuracy are low, so we choose to verify a steel surface defect detection algorithm based on machine vision on this dataset for the problem of defect detection in steel production. A series of improvement measures are carried out in the traditional Faster R-CNN algorithm, such as reconstructing the network structure of Faster R-CNN. Based on the small features of the target, we train the network with multiscale fusion. For the complex features of the target, we replace part of the conventional convolution network with a deformable convolution network. The experimental results show that the deep learning network model trained by the proposed method has good detection performance, and the mean average precision is 0.752, which is 0.128 higher than the original algorithm. Among them, the average precision of crazing, inclusion, patches, pitted surface, rolled in scale and scratches is 0.501, 0.791, 0.792, 0.874, 0.649, and 0.905, respectively. The detection method is able to identify small target defects on the steel surface effectively, which can provide a reference for the automatic detection of steel defects.

scholarly journals Steel Surface Defect Detection using Deep Learning

2020 ◽  
Vol 5 (7) ◽  
pp. 244-250
Author(s):  
Vira Fitriza Fadli ◽  
Iwa Ovyawan Herlistiono
Keyword(s):  
Quality Control ◽  
Deep Learning ◽  
Defect Detection ◽  
High Frequency ◽  
Steel Surface ◽  
Surface Defect ◽  
Detection Algorithm ◽  
Surface Defect Detection ◽  
Stable Performance ◽  
Frequent Problem

Steel defects are a frequent problem in steel companies. Proper quality control can reduce quality problems arising from steel defects. Nowadays, steel defects can detect by automation methods that utilize certain algorithms. Deep learning can help the steel defect detection algorithm become more sophisticated. In this study, we use deep learning CNN with Xception architecture to detect steel defects from images taken from high-frequency and high-resolution cameras. There are two techniques used, and both produce respectively 0.94% and 0.85% accuracy. The Xception architecture used in this case shows optimal and stable performance in the process and its results.

Improved cross pattern approach for steel surface defect recognition

2020 ◽  
Vol 110 (11-12) ◽  
pp. 3091-3100
Author(s):  
Zoheir Mentouri ◽  
Hakim Doghmane ◽  
Abdelkrim Moussaoui ◽  
Hocine Bourouba
Keyword(s):  
Steel Surface ◽  
Surface Defect ◽  
Pattern Approach ◽  
Defect Recognition
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