# Copper Strip Surface Defect Detection Model Based on Deep Convolutional Neural Network
###### Abstract
The defect types of different production sites are different, and the detection environment of the same production site is constantly changing. Thus, the recognition accuracy becomes significantly reduced once the lighting, colour or field of view changes. With the rapid development of artificial intelligence theory and technology, this technique has been successfully applied in many fields [12], which provides new ideas and directions for surface defect detection. At present, some scholars have used deep learning to detect defects on the surface of steel strip and aluminium materials. In the field of supervised learning, [PERSON] et al. [13; 14] established a surface defect dataset of the hot rolling strip and proposed a defect recognition algorithm of a multi-feature fusion convolutional neural network, which realises the recognition of six common surface defects of the hot rolling strip. [PERSON] et al. [15] combined traditional machine learning with convolutional neural networks and proposed an automatic classification method for surface defects of the hot rolling strip. The optimal parameters were obtained through a large number of experiments, which can realise defect classification. [PERSON] et al. [16] proposed an improved Faster RCNN aluminium profile surface defect recognition method by introducing a feature pyramid structure and realised the detection of ten surface defect types of the aluminium profile. [PERSON] et al. [17] improved the YOLOv3 model by changing the number of anchors, which improved the detection performance of small defects on the surface of aluminium profiles. [PERSON] et al. [18] firstly used the ViBe algorithm to segment the defect area from the image and then utilised median filtering and morphology operations to extract the defect area accurately. Finally, they realised the identification and classification of the surface defects of the aluminium strip through the CNN. In the field of semi-supervised learning, [PERSON] et al. [19] established a PLCNN semi-supervised learning recognition model of strip surface defects on the NEU dataset and indicated that this method can save the amount of data labelling and improve efficiency, which is suitable for label-restricted defect recognition tasks. [PERSON] et al. [20] used a generative adversarial network to generate a large number of unlabelled defect image samples and proposed a multiple training method based on cDCGAN and Resnet18, which substantially improved the accuracy of strip surface defect recognition, compared with previous methods. When traditional machine vision is used for defect recognition and classification, there are three steps that are generally required: feature design, extraction, and classification. Among them, feature design is the foundation; the common features include colour, brightness, shape, and texture. The specific features that are used highly depend on the designers' domain knowledge, which usually requires a lot of trial-and-error experiments to find a better feature combination. The detection accuracy of final model is directly related to the quality of the feature design. When the defect feature can be accurately described, and the repetition rate of defects is high, the traditional machine vision can achieve ideal performance. There are many types of copper strip surface defects, which are usually classified by defect generation mechanism. The shape and location of the same types of defects are difficult to accurately describe; the appearance of some different types of defects have similar features. At the same time, the detection environment of the production site is constantly changing, which presents great obstacles in the application of traditional machine vision. Compared with traditional machine vision, the primary advantage of deep learning is that it does not require manual feature design, but uses intelligent methods to learn, extract, and classify the basic features of image automatically.