# Defect Detection Methods for Industrial Products Using Deep Learning Techniques: A Review
## 3 Deep Learning Surface Defect Detection Methods for Industrial Products
### Semi-Supervised  
As a result of combining the properties of supervised and unsupervised methods, semi-supervised methods are developed. Only normal samples are used as training data for semi-supervised defect detection and a defect-free boundary is learned and set, and any samples outside the boundary are considered anomalous. Since there are few defective samples to be obtained, the method is extremely useful. Nevertheless, this method has lower accuracy in defect detection compared to supervised methods. Unlabeled sample data can be automatically generated by semi-supervised methods without manual intervention. A framework for identifying defects in PCB solder joints was proposed in literature [54], which utilizes a combination of active learning and self-training through a sample query suggestion algorithm for classification. The framework has been demonstrated to improve classification accuracy while reducing the need for manual annotations. A semi-supervised model of convolutional autoencoder (CAE) and generative adversarial network is proposed in [55]. After training with unlabeled data, the stacked CAE's encoder network is retained and input into the SoftMax layer as a GAN discriminator. Using GAN, false images of steel surface defects were generated to train the discriminator. For the detection of steel surface defects, literature [56] developed a WSL framework combining localization networks (LNets) and decision networks (DNets), with LNets trained by image level labels and outputs a heat map of potential defects as input to DNets. Through the use of the RSAM algorithm to weight the regions identified by LNet, the proposed framework has been demonstrated to be effective on real industrial datasets. The application prospects for weakly supervised methods are also wide because the methods simultaneously com  
Figure 5: Presenting the results of experiments on six defect samples using four methods. The defect types are listed in the first column and include drops tar, shadow, floating, crush, pitted surface and scratch. The results from traditional manual feature extraction methods (CPICS-LBP, AEC-LBP, HWV and the proposed method in [53]) are shown in columns 2-5. The experiment compares the proposed method with current state-of-the-art methods in detecting strip steel surface defects. bine advantages of both supervised and unsupervised methods. There are few weakly supervised methods for detecting surface defects in industrial products. The literature [57] proposed a deep learning algorithm to learn defects from a variety of defect types with an unbalanced training sample pool for PCBA manufacturing products. In this method, an overall defect recognition accuracy of 98% is achieved in PBCA images using a novel batch sampling method and the sample weighted cost function. A semi-supervised learning system that generates samples to detect surface defects was proposed according to the literature [58]. As part of the semi-supervised learning part, two CDCGAN and ResNet18 classifiers were used, and the NEU-CLS dataset was used to compare the two classifiers. In this way, supervised learning and transfer learning are both shown to be inferior to the method. A convolutional neural network structure based on residual network structures was proposed in [59] by stacking two layers of residual building modules together, resulting in a 43-layer convolutional neural network, while at the same time by appropriately increasing the network width; a more balanced network depth and network width can be obtained and accuracy can be improved. The network structure shows good performance on the DAGM, NEU steel, and copper clad plate datasets. Table 3 provides an overview of recent research in surface defect detection, including classifications of targets and Table 4 evaluates the strengths and weaknesses of deep learning techniques for detecting surface defects in industrial products, including accuracy, computational efficiency, and robustness.