Periodic Surface Defect Detection in Steel Plates Based on Deep Learning

It is difficult to detect roll marks on hot-rolled steel plates as they have a low contrast in the images. A periodical defect detection method based on a convolutional neural network (CNN) and long short-term memory (LSTM) is proposed to detect periodic defects, such as roll marks, according to the strong time-sequenced characteristics of such defects. Firstly, the features of the defect image are extracted through a CNN network, and then the extracted feature vectors are inputted into an LSTM network for defect recognition. The experiment shows that the detection rate of this method is 81.9%, which is 10.2% higher than a CNN method. In order to make more accurate use of the previous information, the method is improved with the attention mechanism. The improved method specifies the importance of inputted information at each previous moment, and gives the quantitative weight according to the importance. The experiment shows that the detection rate of the improved method is increased to 86.2%.

[1]  Shihai Zhang,et al.  Surface Defects Inspection Method for the Medium and Heavy Plate , 2016 .

[2]  Xin Zhang,et al.  TFX: A TensorFlow-Based Production-Scale Machine Learning Platform , 2017, KDD.

[3]  Reinhold Huber-Mörk,et al.  Convolutional Neural Networks for Steel Surface Defect Detection from Photometric Stereo Images , 2014, ISVC.

[4]  Sang Jun Lee,et al.  Vision-based surface defect inspection for thick steel plates , 2017 .

[5]  Di He,et al.  Defect detection of hot rolled steels with a new object detection framework called classification priority network , 2019, Comput. Ind. Eng..

[6]  Wu Bo,et al.  Anomaly detection in smart grid based on encoder-decoder framework with recurrent neural network , 2017 .

[7]  Alexander Wong,et al.  Fast YOLO: A Fast You Only Look Once System for Real-time Embedded Object Detection in Video , 2017, ArXiv.

[8]  Houshang Darabi,et al.  LSTM Fully Convolutional Networks for Time Series Classification , 2017, IEEE Access.

[9]  Wenhao Yu,et al.  An attention mechanism based convolutional LSTM network for video action recognition , 2019, Multimedia Tools and Applications.

[10]  JingLin Chen,et al.  An Ensemble of Convolutional Neural Networks for Image Classification Based on LSTM , 2017, 2017 International Conference on Green Informatics (ICGI).

[11]  Yulei Rao,et al.  A deep learning framework for financial time series using stacked autoencoders and long-short term memory , 2017, PloS one.

[12]  Rajalingappaa Shanmugamani,et al.  Detection and classification of surface defects of gun barrels using computer vision and machine learning , 2015 .

[13]  Li Yi,et al.  An End‐to‐End Steel Strip Surface Defects Recognition System Based on Convolutional Neural Networks , 2017 .

[14]  Xuelong Li,et al.  Video Summarization With Attention-Based Encoder–Decoder Networks , 2017, IEEE Transactions on Circuits and Systems for Video Technology.

[15]  Shubin Li,et al.  Detection of rail surface defects based on CNN image recognition and classification , 2018, 2018 20th International Conference on Advanced Communication Technology (ICACT).

[16]  Abhishek Verma,et al.  Compressed residual-VGG16 CNN model for big data places image recognition , 2018, 2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC).

[17]  Joachim M. Buhmann,et al.  Wheel Defect Detection With Machine Learning , 2018, IEEE Transactions on Intelligent Transportation Systems.

[18]  Amy Loutfi,et al.  A Deep Learning Approach with an Attention Mechanism for Automatic Sleep Stage Classification , 2018, ArXiv.

[19]  Dit-Yan Yeung,et al.  Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting , 2015, NIPS.