# Defect Detection Methods for Industrial Products Using Deep Learning Techniques: A Review
## 2 Introduction
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Approach** & **Reference** & **Method** & **Strengths** & **Weaknesses** \\ \hline \multirow{8}{*}{[2]} & \multirow{2}{*}{[3]} & Color histogram and vector texture feature & \begin{tabular}{l} Proven to be effective with defects \\ involving junctions; able to handle \\ multiple input features \\ \end{tabular} & \begin{tabular}{l} Not be suitable for detecting defects in \\ textures with complex patterns; may \\ not work well for defects that do not \\ involve changes in color \\ May require additional preprocessing \\ steps, such as image enhancement \\ techniques, to improve their \\ performance; do not have the ability \\ to learn from data as compared to \\ deep learning based methods, which \\ can make them less adaptable to new \\ types of defects or variations in the \\ data \\ \end{tabular} & \begin{tabular}{l} Not be suitable for detecting defects in \\ textures with complex patterns; may \\ not work well for defects that do not \\ involve changes in color \\ May require additional preprocessing \\ steps, such as image enhancement \\ techniques, to improve their \\ performance; do not have the ability \\ to learn from data as compared to \\ deep learning based methods, which \\ can make them less adaptable to new \\ types of defects or variations in the \\ data \\ \end{tabular} \\ \multirow{8}{*}{[4]} & \multirow{2}{*}{\begin{tabular}{l} SVM-based and \\ color \\ histogram-based \\ \end{tabular} } & \begin{tabular}{l} High accuracy rate; able to extract \\ useful information from the color of \\ an image, which can be useful for \\ detecting defects that are based on \\ color variations, such as stains or \\ discolorations \\ \end{tabular} & \begin{tabular}{l} May not perform well with other \\ types of materials; may not be able to \\ detect defects with low contrast \\ May not perform well with defects \\ that do not involve color changes; not \\ be able to detect defects with low \\ contrast \\ \end{tabular} \\ \multirow{8}{*}{[5]} & \multirow{2}{*}{\begin{tabular}{l} Color moment \\ features and FSIFT \\ features \\ \end{tabular} } & \begin{tabular}{l} Successful in resolving tile surface \\ defect problem not being adequately \\ described by a single feature \\ \end{tabular} & \begin{tabular}{l} May not perform well with defects \\ that do not involve color changes; not \\ be able to detect defects with low \\ contrast \\ \end{tabular} \\ \hline \multirow{8}{*}{[6]} & \multirow{2}{*}{\begin{tabular}{l} Fourier transform \\ and Hough \\ transform \\ \end{tabular} } & \
\begin{tabular}{l} Good at detecting periodic patterns, \\ which can be useful for detecting \\ defects in materials with repeating \\ patterns, such as in fabrics or metals \\ \end{tabular} &
\begin{tabular}{l} Do not have the ability to learn from \\ data as compared to deep learning \\ based methods, which can make them \\ less adaptable to new types of defects \\ or variations in the data \\ Is not as good at detecting defects in \\ images with low contrast or noise, \\ which can make it less effective in \\ some industrial applications; does not \\ have the ability to learn from data as \\ compared to deep learning based \\ methods, which can make it less \\ adaptable to new types of defects or \\ variations in the data \\ Limited to detecting specific types of \\ defects (based on the specific \\ templates or reconstruction of the \\ Fourier domain), which can make \\ them less suitable for more complex or \\ varied defects \\ \end{tabular} \\ \hline \end{tabular}
\end{table} 
Table 2: _Cont._commercial and industrial applications, such as in the automotive industry for detecting defects in cars. The deep-learning-based surface defect detection software is employed in these settings to improve the efficiency and accuracy of the defect detection process. Recently, several papers covering the latest techniques, applications, and other aspects have been published on deep learning in defect detection [19].