期刊
MATERIALS
卷 13, 期 20, 页码 -出版社
MDPI
DOI: 10.3390/ma13204629
关键词
surface defect classification; multiple image scales; convolutional neural networks; classification accuracy; latency
类别
资金
- National Natural Science Foundation of China [51875456]
- Natural Science Basic Research Plan in Shaanxi Province of China [2019JM-450]
- Scientific Research Program - Shaanxi Provincial Education Department [20JC029]
- China Scholarship Council (CSC)
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.
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