A New Steel Defect Detection Algorithm Based on Deep Learning.

Wei Cheng,Dan Li,Weidong Zhao,Mario Versaci,Feng Chen,Hancheng Huang

doi:10.1155/2021/5592878

Wei Cheng, Dan Li + Show 4 more

Open Access

https://doi.org/10.1155/2021/5592878

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Abstract

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.

Highlights

It will produce a variety of defects in the steel rolling process due to the environment, production process, and other restrictions, which have a certain influence on the wear resistance and toughness of steel
Due to the high temperature and long time, it will lead to surface defects pitted surface, that is, coarse grains and other phenomena. ere will be an oxide layer and rust on the surface when the steel is exposed to air for a long time, and the steel will contact with the air at a high temperature to form the rolling scale [1] in the rolling process
E traditional steel surface defect detection [2,3,4] is completed by manual visual inspection combined with traditional machine vision [5,6,7]. ere are some shortcomings in manual testing, such as low confidence and high labor intensity. e traditional target detection [8] selects candidate regions on a given image; the features are extracted manually and the trained classifier is used for classification. is method has high time complexity and low precision and is difficult to meet the actual production needs of the steel industry

Summary

Introduction

It will produce a variety of defects in the steel rolling process due to the environment, production process, and other restrictions, which have a certain influence on the wear resistance and toughness of steel. With the continuous development of the convolutional neural network [9], target detection based on deep learning has become the mainstream surface defect detection method.

Results

Conclusion