A Light Weight Deep Learning Model with Multi Scale Features for Steel Surface Defect Classification
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.
Materials