# Copper Strip Surface Defect Detection Model Based on Deep Convolutional Neural Network
###### Abstract  
Surface defect automatic detection has great significance for copper strip production. The traditional machine vision for surface defect automatic detection of copper strip needs artificial feature design, which has a long cycle, and poor ability of versatility and robustness. However, deep learning can effectively solve these problems. Therefore, based on the deep convolution neural network and the transfer learning strategy, an intelligent recognition model of surface defects of copper strip is established in this paper. Firstly, the defects were classified in accordance with the mechanism and morphology, and the surface defect dataset of copper strip was established by comprehensively adopting image acquisition and image augmentation. Then, a two-class discrimination model was established to achieve the accurate discrimination of perfect and defect images. On this basis, four CNN models were adopted for the recognition of defect images. Among these models, the EfficientNet model through transfer learning strategy had the best comprehensive performance with a recognition accuracy rate of 93.05%. Finally, the interpretability and deficiency of the model were analysed by the class activation map and confusion matrix, which point toward the direction of further optimization for future research. copper strip; surface defects; deep learning; intelligent detection; visualisation +
Footnote †: journal: Medical Imaging  
**Citation:** [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Copper Strip Surface Defect Detection Model Based on Deep Convolutional Neural Network. **Appl. Sci. 2021**, _11_, 8945. [[https://doi.org/10.3390/app11198945](https://doi.org/10.3390/app11198945)]([https://doi.org/10.3390/app11198945](https://doi.org/10.3390/app11198945))  
**Citation:** [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Copper Strip Surface Defect Detection Model Based on Deep Convolutional Neural Network. **Appl. Sci. 2021**, _11_, 8945. [[https://doi.org/10.3390/app11198945](https://doi.org/10.3390/app11198945)]([https://doi.org/10.3390/app11198945](https://doi.org/10.3390/app11198945))  
**Citation:** [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Copper Strip Surface Defect Detection Model Based on Deep Convolutional Neural Network. **Appl. Sci. 2021**, _11_, 8945. [[https://doi.org/10.3390/app11198945](https://doi.org/10.3390/app11198945)]([https://doi.org/10.3390/app11198945](https://doi.org/10.3390/app11198945))  
defect image segmentation to extract five features of defects (aspect ratio, perimeter, area, circularity and centre of gravity) and established a classifier to achieve defect recognition, using a single hidden layer BP neural network. [PERSON] [10] proposed the MM-Canny defect segmentation algorithm based on the improved Canny edge detection operator and morphology method and established a support vector machine classification model to achieve defect classification by extracting three feature types of geometry (area and diameter ratio of length and short), grey (average grey, variance, slope, and defect area energy), and texture (corner second-order matrix, contrast, correlation, and entropy). [PERSON] et al. [11] initially divided the copper strip defect image into several sub-images and then divided the sub-images into several wavelet units to obtain the wavelet statistical results of the defect images. Such an approach achieved extraction of the defect features, and the support vector model was adopted to complete defect classification. Some progress can generally be realised for surface defect classification and recognition of copper strip by using traditional machine vision, but some unsolved problems remain. The traditional machine vision generally requires manual feature design (feature engineering) before defect recognition. The final defect recognition accuracy is directly related to the quality of the feature design, which is highly dependent on professional knowledge and has poor versatility. In addition, the robustness of traditional machine vision is poor.