# Defect Detection Methods for Industrial Products Using Deep Learning Techniques: A Review
## 2 Introduction
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Approach** & **Reference** & **Method** & **Strengths** & **Weaknesses** \\ \hline \multirow{4}{*}{\begin{tabular}{c} **Fuzzy model** \\ **[**11**]** \\ \end{tabular} } & [10] & \begin{tabular}{c} Multi-block local \\ binary pattern \\ (LBP) algorithm \\ \end{tabular} & \begin{tabular}{c} High recognition accuracy and meets \\ online real-time detection \\ requirements; robust to rotation and \\ scaling; fast processing time \\ \end{tabular} & \begin{tabular}{c} Does not perform well with defects \\ that do not involve texture changes; \\ may not be able to detect defects with \\ low contrast; \\ Not as good at detecting defects that \\ have a very different texture than the \\ one used to train the model; may not \\ be as accurate as deep learning-based \\ methods, which can learn from data \\ and adapt to new types of defects \\ \end{tabular} \\ \multirow{4}{*}{\begin{tabular}{c} **Fuzzy model** \\ **[**11**]** \\ \end{tabular} } & [11] & \begin{tabular}{c} Fuzzy model based \\ on GLCM \\ extraction \\ \end{tabular} & \begin{tabular}{c} Can be useful for detecting defects in \\ images with low contrast or noise, \\ where other methods may fail \\ \end{tabular} & \begin{tabular}{c} Can be useful for detecting defects in \\ images with low contrast or noise, \\ where other methods may fail \\ \end{tabular} & \begin{tabular}{c} Does not perform well with defects \\ that do not involve texture changes; \\ may not be able to detect defects with \\ low contrast; \\ Not as good at detecting defects that \\ have a very different texture than the \\ one used to train the model; may not \\ be as accurate as deep learning-based \\ methods, which can learn from data \\ and adapt to new types of defects \\ \end{tabular} \\ \multirow{4}{*}{\begin{tabular}{c} Reduced \\ Coordinated \\ Cluster \\ Representation \\ (RCCR) \\ \end{tabular} } & \
\begin{tabular}{c} Good at detecting defects with high \\ precision, as it is able to extract \\ features of the defects and identify \\ them; good at detecting defects in \\ images with low contrast or noise, as \\ it is able to extract features that are \\ robust to these challenges \\ \end{tabular} &
\begin{tabular}{c} It is limited to detecting specific types \\ of defects (based on the specific \\ clustering method and feature \\ extraction technique used), which can \\ make it less suitable for more complex \\ or varied defects \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: An overview of the strengths and weaknesses of various feature-based methods for detecting surface defects in industrial products. Using only one feature or one class of features on industrial products is rarely sufficient because their surfaces typically contain a variety of information. Consequently, many features are used in combination in practical applications, making it difficult to detect defects. Additionally, feature-based approaches are highly effective when they detect defects in images with little or no variation, and when defects appear on surfaces in a consistent pattern. Considering the wide range of uncertainties in industrial settings, it is important to develop methods that are adaptable to such wide ranges of variations in defect intensity, shape, and size. Deep learning models based on convolutional neural networks (CNN) have had a lot of success in various computer vision fields, such as recognizing faces, identifying pedestrians, detecting text in images, and tracking targets. Additionally, these models are used in a wide range of industrial settings for defect detection. This includes both