# Defect Detection Methods for Industrial Products Using Deep Learning Techniques: A Review
## 3 Deep Learning Surface Defect Detection Methods for Industrial Products
### Supervised  
Supervised detection requires large datasets of defect-free and defective samples labeled in a training set. Since all the training data is labeled, detection rates can be very high. It must be noted, however, that supervised detection may not always the most effective approach due to the imbalance of classes in the datasets. There are a number of datasets that supervised learning methods use, including the fabric dataset [30], rail defect dataset [31], and railroad dataset [32]. Deep neural networks and feature extraction and classification methods used in supervised methods differ in their structures. For example, detecting cross-category defects without retraining was proposed using a two-layer neural network in the literature [33]. Based on structural similarities between image pairs, the method learns differential features, which may result in some structural similarities among different classification objects. This method has been shown to be able to detect defects in different types of factories based on experiments in real factory datasets. Literature [34] suggests that the composition of kernelsis more important than the number of layers when it comes to detection results. To detect small defects and textures in surface images, it is necessary to use a sample image that is large enough for computational accuracy and reducing the cost of the network. ShuffleNet uses convolution of pointwise groups and channel shuffle as two new techniques to achieve this goal. Literature [35] proposes a novel in-line inspection system for plastic containers based on ShuffleNet V2. The system can be used to inspect images on complex backgrounds as well. In [36], they proposed ShuffleDefectNet, a deep-learning-based defect detection system that achieved 99.75% accuracy on the NEU dataset. Reference [37] suggested that shallow CNN networks can be used to identify anomalies. To train the model, only negative images are used and the research employs full-size images. The argument is that it is not necessary to have full-size examples of both defective and defect-free samples, as the negative samples already have pixels that correspond to the defect-free regions. Based on the Fast R-CNN model, Faster R-CNN introduces a region proposal network (RPN), which enables an end-to-end learning algorithm. This leads to a near-costless regional recommendation algorithm that significantly improves the speed of target detection. Faster R-CNN was used in [38] to detect PCB surface defects, a new network was proposed combining ResNet50, GRAPN residual units, and ShuffleNetV2. Using a cascaded RCNN structure, as described in literature [39], the defect detection problem of power line insulators can be changed into a two-level target detection problem; the results are shown in Figure 4.  
In limited hardware configurations, MobileNet-SSD [9] improves real-time object detection performance. There is no need to sacrifice accuracy for the reduction of parameters in this network. An SSD network classifies regression and boundary box regression using various convolution layers. Translation invariance and variability are resolved in this model, resulting in good detection precision and speed. Object detection is effective when defects have regular or predictable shapes [40]. Additional preprocessing steps can be applied to more complex defect types. Fully Convolutional Networks (FCNs) use all convolutional layers as network layers; label maps can be directly derived using pixel-level prediction. To achieve accurate results, a deconvolution layer with larger data sizes is used. In literature [41], FCN and Faster R-CNN were combined to develop a deep learning model that could detect stains, leaks, and pipeline blockages in tunnels. A method for segmenting defects in solar cell electroluminescence pictures was presented in [42]. A defect segmentation map was obtained in one step by combining FCN with a specific U-net architecture. Figure 4: The results of insulator defect detection.