# Defect Detection Methods for Industrial Products Using Deep Learning Techniques: A Review
## 3 Deep Learning Surface Defect Detection Methods for Industrial Products
### Semi-Supervised
These tables give a comprehensive understanding of current research and the considerations for using deep learning in surface defect detection. Table 5 lists a selection of commonly used datasets for training and testing algorithms for detecting surface defects in industrial products. The datasets are classified based on the type of industrial products they are intended for. This information is useful for researchers and practitioners looking for suitable datasets for their work in the field of surface defect detection. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Year** & **Method** & **Target** & **Performance** \\ \hline \multirow{4}{*}{[46]} & \multirow{4}{*}{2022} & \multirow{4}{*}{PatchCore} & MVTec benchmark datasets, the ShanghaiTech Campus dataset (STC), and the Magnetic Tile Defects dataset (MTD) & Demonstrated a high level of performance on the MVTec dataset with an AUROC of over 99\% and a particularly strong ability to perform well with small training sets \\ \cline{1-1} \cline{3-5}  & & & & \\ \cline{1-1} \cline{3-5}  & & & & \\ \cline{1-1} \cline{3-5}  & & & & \\ \cline{1-1} \cline{3-5}  & & & & \\ \cline{1-1} \cline{3-5}  & & & & \\ \cline{1-1} \cline{3-5}  & & & & \\ \hline \multirow{3}{*}{[60]} & \multirow{3}{*}{2019} & \multirow{3}{*}{CNN} & \multirow{3}{*}{Steel} & This method achieves significantly higher recognition accuracy for steel surface defects than state-of-the-art classifiers \\ \cline{1-1} \cline{3-5}  & & & & \\ \cline{1-1} \cline{3-5}  & & & & \\ \cline{1-1} \cline{3-5}  & & & & \\ \hline \multirow{3}{*}{[55]} & \multirow{3}{*}{2019} & \multirow{3}{*}{GAN} & \multirow{3}{*}{Steel} & CAESGAN achieves the best classification rate compared to traditional methods, especially for hot rolled plates \\ \cline{1-1} \cline{3-5}  & & & & \\ \cline{1-1} \cline{3-5}  & & & & \\ \cline{1-1} \cline{3-5}  & & & & \\ \hline \multirow{3}{*}{[61]} & \multirow{3}{*}{2019} & \multirow{3}{*}{SDD and ResNet} & \multirow{3}{*}{Steel} & Steel surface defect detection can be performed with high speed and accuracy \\ \cline{1-1} \cline{3-5}  & & & & \\ \cline{1-1} \cline{3-5}  & & & & \\ \hline \multirow{3}{*}{[62]} & \multirow{3}{*}{2019} & \multirow{3}{*}{Faster-RCNN} & \multirow{3}{*}{Steel} & Achieved higher detection accuracy and more accurate location of defects, especially for tiny and slender defects \\ \cline{1-1} \cline{3-5}  & & & & \\ \cline{1-1} \cline{3-5}  & & & & \\ \hline \multirow{3}{*}{[63]} & \multirow{3}{*}{2018} & \multirow{3}{*}{CNN} & DAGM dataset & Can achieve a 99.8\% accuracy rate in detecting defects \\ \cline{1-1} \cline{3-5}  & & & & \\ \cline{1-1} \cline{3-5}  & & & & \\ \hline \multirow{3}{*}{[64]} & \multirow{3}{*}{2016} & \multirow{3}{*}{CNN} & DAGM dataset & This method demonstrates a low false alarm rate and excellent defect detection results \\ \cline{1-1} \cline{3-5}  & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: An overview of recent research publications as well as classifications based on targets. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Year** & **Method** & **Target** & **Performance** \\ \hline
[65] & 2019 & FCN & DAGM dataset & \begin{tabular}{c} A defect image (512 \(\times\) 512) can be processed each second, with more than \\ 99\% of pixel accuracy \\ \end{tabular}  \\ \hline
[66] & 2017 & \ \begin{tabular}{c} 2-stage FCN \\ framework \\ \end{tabular}  & DAGM dataset & \begin{tabular}{c} Able to achieve meaningful results in \\ terms of performance and speed \\ \end{tabular}  \\ \hline
[34] & 2016 & CNN & Texture & \begin{tabular}{c} In comparison to traditional manual \\ inspection systems, this method offers \\ several advantages in time and cost \\ savings \\ \end{tabular}  \\ \hline
[67] & 2018 & AutoEncoder & Various materials & \begin{tabular}{c} Compared to traditional hand-engineered \\ feature extraction methods, this approach \\ is more generic \\ \end{tabular}  \\ \hline
[68] & 2020 & CNN &