Hot-Rolled Steel Strip Surface Inspection Based on Transfer Learning Model

• Main Authors: Hao Wu, Quanquan Lv
• Format: Article
• Language: English
• Published: Hindawi Limited 2021-01-01
• Series: Journal of Sensors
• Online Access: http://dx.doi.org/10.1155/2021/6637252

In the production process of steel strips, the detection of surface defects is very important. However, traditional methods of defect detection bring problems of low detection accuracy and dependence on subjective judgment. In this study, the surface defects of steel strips are detected by a classic convolutional neural network method that is improved by the use of a transfer learning model. This model has the advantages of shorter training time, faster convergence, and more accurate weight parameters. The transfer learning model obtained through experiments secures better results in defect detection than the classic convolutional neural network method, as its accuracy of training and testing has reached about 98%. Finally, a model based on a full convolutional neural network (FCN) is proposed for segmenting the defective areas of steel strips.