1. A Light-Weight Deep-Learning Model with Multi-Scale Features for Steel Surface Defect Classification.
- Author
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Liu, Yang, Yuan, Yachao, Balta, Cristhian, and Liu, Jing
- Subjects
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CONVOLUTIONAL neural networks , *SURFACE defects , *MULTISCALE modeling , *STEEL , *CLASSIFICATION , *COMPUTER vision - Abstract
Automatic inspection of surface defects is crucial in industries for real-time applications. Nowadays, computer vision-based approaches have been successfully employed. However, most of the existing works need a large number of training samples to achieve satisfactory classification results, while collecting massive training datasets is labor-intensive and financially costly. Moreover, most of them obtain high accuracy at the expense of high latency, and are thus not suitable for real-time applications. In this work, a novel Concurrent Convolutional Neural Network (ConCNN) with different image scales is proposed, which is light-weighted and easy to deploy for real-time defect classification applications. To evaluate the performance of ConCNN, the NEU-CLS dataset is used in our experiments. Simulation results demonstrate that ConCNN performs better than other state-of-the-art approaches considering accuracy and latency for steel surface defect classification. Specifically, ConCNN achieves as high as 98.89% classification accuracy with only around 5.58 ms latency over low training cost. [ABSTRACT FROM AUTHOR]
- Published
- 2020
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