# Copper Strip Surface Defect Detection Model Based on Deep Convolutional Neural Network
## 5 Conclusions  
1. Combining the practical requirement, the common surface defects of copper strip were divided into 8 classifications: line mark (LM), black spot (BS), concave-convex pit (CP), edge crack (EC), hole (Ho), insect spot (IS), peeling (Pe), and smudge (Sm). Image data were collected in a production line, and a dataset of copper strip surface defects was established (YSU_CSC).
2. The gray values of the perfect image clustered and distributed around the median value of 127.5, whilst a part of the gray values of the defect image was distributed in the range of <100 and >200. The perfect and defect images could be quickly distinguished.
3. Compared with the performance of VGG16, MobileNetV2, and ResNet50 on the same testing set, the surface defect recognition model of copper strip based on the  
Figure 12: Class activation maps of surface defects localised by the proposed model: (**a**) original defect image; (**b**) class activation maps.  
Figure 13: Confusion matrix of the training and testing sets by the proposed model: (**a**) training set, (**b**) testing set.  
EfficientNet convolutional neural network had the highest accuracy, reaching 93.05%. The average recognition time of a single defect image was 197 ms. The model has a good generalisation capability, and its calculation speed is fast, which can meet the actual engineering requirements and has the best overall performance.
* On the testing set, the model improved the overall recognition performance on LM, CP, and Sm, with low error rates of 0%, 4.44%, and 2.22%, respectively. The defect recognition error rates for the three classifications of BS, Ho, and IS were relatively high at 8.89%, 15.56%, and 11.11%, respectively. The CAM shows that the model learned the key feature information for various classifications of defects. Consideration will be given in the future to further improve the overall performance of the model from three aspects: increasing the number of defect image data, subdividing similar defective images, and improving the model structure.  
Conceptualisation, Y.X. and D.W.; methodology, Y.X. and D.W.; writing--original draft preparation, Y.X. and D.W.; writing--review and editing, Y.X. and B.D.; supervision, H.Y. and B.D.; funding acquisition, D.W. and H.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science foundation of China (Grant No. 52074242), and the High-end Talents and \"Giant Plan\" Innovation Team of Hebei Province. (Grant No. 2019).  
Not applicable.  
The authors declare no conflict of interest.  
The following abbreviations and symbols are used in this manuscript:  
\begin{tabular}{l l} Abbreviation & Description \\ LM & Line Mark \\ BS & Black Spot \\ CP & Concave-convex Pit \\ EC & Edge Crack \\ Ho & Hole \\ IS & Insect Spot \\ Pe & Peeling \\ Sm & Smudge \\ YSU\_CSC & A Surface Defect Dataset of Copper Strip \\ CNN & Convolution Neural Network \\ MBConv & Mobile Inverted Bottleneck Convolution Module Layers, \\ VGG & Visual Geometry Group Network \\ MobileNet & Efficient Convolutional Neural Networks for Mobile Vision Applications \\ ResNet & Residual Network \\ EfficientNet & Variable Model Scale Convolution Neural Network \\ CAM & Class Activation Mapping \\ Symbols & Description \\ \(f\) & Original image \\ \(n\) & The added noise \\ \(g\) & Image after adding noise \\ \(p(n)\) & Probability density function of noise \\ \((x_{0},y_{0})\) & Coordinate of any point in the original image \\ \((x,y)\) & The coordinate after the rotation angle \(\theta\) becomes \\ \(\Omega\) & Gray values in the original image \\ \(\Omega^{\prime}\) & Gray value after brightness adjustment \\ \end{tabular}