# Defect Detection Methods for Industrial Products Using Deep Learning Techniques: A Review
## 3 Deep Learning Surface Defect Detection Methods for Industrial Products
### Unsupervised  
Research has begun to explore unsupervised methods to overcome the disadvantages of supervised methods. By learning the inherent characteristics of the input training data, the machine can learn some of its own characteristics and connections when there is no label information and automatically classifies the input training data based on the pattern of these unlabeled data [43]. It automatically classifies these unlabeled data based on inherent characteristics and connections between the data. Methods based on reconstruction and embedding similarity are the most commonly used to detect surface defects among unsupervised learning methods. Reconstruction-based methods such as autoencoders (AEs) and Generative Adversarial Networks (GANs) are most commonly used. Popular algorithms include PaDIM [44], SPADE [45] PatchCore [46], etc. In [47], an algorithm based on DBN was proposed for detecting defects in solar cells. Both training and reconstructed images were used as supervision data by the fine-tuning network of the BP algorithm. Literature [48] proposed a multi-scale convolutional denoising autoencoder with high accuracy and robustness that synthesizes the results of multiple pyramid levels.  
A SOM-based detection method was proposed in [49] for determining the difference between normal and defective wood. The first stage involves detecting suspected defect areas, and the second stage involves separately inspecting each defect area. A detection method that uses GANs was proposed in reference [50]. The method is divided into two stages: first, a generative network and a learning mechanism based on statistical representation are used to detect new areas. In the second stage, defects and normal samples are directly distinguished using the Frechet distance. The solar panel dataset was used to test the method, and it achieved 93.75% accuracy.  
A multiscale AE with fully convolutional neural networks has been proposed [51], in which each FCAE sub-network directly obtains the original feature image from the input image and performs feature clustering. Utilizing a fully convolutional neural network, the residual images were combined to create the defect image. PatchCore, introduced in literature [46], is a technique for identifying and isolating abnormal data in scenarios where only normal examples are available. It balances the need to retain normal context through memory banks of patch-level features extracted from pre-trained ImageNet networks and minimize computational time via coreset subsampling to create a leading system for cold-start image anomaly detection and localization that is efficient on industrial benchmarks. On MVTec, the algorithm demonstrated an AUROC of over 99%, while also being highly efficient in small training set scenarios. Literature [52] presented a GAN-based surface vision detection framework that uses OTSU to segment fusion feature response maps and fuses the responses of the three layers of the GAN discriminator. The framework has been proven effective on datasets of wood cracks and road cracks. As shown in Figure 5, ref. [53] proposed a GAN-based method for detecting strip steel surface defects, in which the generator G uses encoding and the hidden space features in the penultimate layer are fed into a SVM to detect defects. The test results on images provided by the Handan Iron and Steel Plant indicated good accuracy. It is more effective at detecting texture images; however, its accuracy still needs to be improved.