# Surface Defect Detection Methods for Industrial Products: A Review
## 2 Traditional Feature-Based Machine Vision Algorithm for Surface Defect Detection
### Color Feature-Based Method
#### 2.2.1 Color Histograms  
The color histogram describes the proportion of different colors in the entire image, which is the result of global statistics; it does not pay attention to the spatial position of the color and cannot describe the objects in the image.  
Features: Insensitive to physical transformations (rotation, scaling, etc.); if the image has multiple areas with obvious differences in the color distribution between the foreground and the background, the color histogram will show double peaks.  
Literature [27] proposed a similarity evaluation method based on color histogram for Electrical Resistance Tomography (ERT) image evaluation. For the defect detection of wood surface, literature [28] proposed a classification method based on the percentage color histogram feature and feature vector texture feature of image block; this method has been  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Reference** & **Feature** & **Target** & **Defect Types** & **Performance** \\ \hline \multirow{8}{*}{Statistical method} & [PERSON] et al. (2015) [16] & Histogram feature & Wood & Knot defect & Recognition rate: 99.8\% \\  & [PERSON] et al. (2017) [17] & Gray level co-occurrence matrix & \multirow{2}{*}{Ceramic} & \multirow{2}{*}{Surface with defects or not} & \multirow{2}{*}{Accuracy rate: 92.31\%} \\  & [PERSON] et al. (2019) [18] & Local binary pattern & Steel plate & Cracks, scratches, indentations, pits, scales & Recognition accuracy: 94.40\% \\ \cline{1-1}  & [PERSON] et al. (2015) [19] & Autocorrelation function and GLCM & Yarm-dyed fabric & Wrong weft, weft crack, oil stain, etc. & Can achieve accurate detection of common defects of yarn-dyed fabric \\ \cline{1-1}  & [PERSON] et al. (2009) [20] & Mathematical morphological & Steel billet & Slender scratches & Detection ratio 87.5\%, can suitable for billet images with scale \\ \hline \multirow{8}{*}{Signal processing method} & [PERSON] et al. (2012) [21] & Fourier transform & Solar cell & Small cracks, breaks, and finger interruptions & Can effectively detect various defects and be implemented for online, real-time defect inspection \\ \cline{1-1}  & [PERSON] et al. (2009) [22] & Gabor filter method & Steel billet & Crack & Accuracy: corner crack: 93.5\%; thin crack: 91.9\% \\ \cline{1-1}  & [PERSON] et al. (2010) [23] & Wavelet transform & Integrated circuit chip & Surface defects & Can detect defects of an IC \\ \hline \multirow{8}{*}{Structural method} & [PERSON]- (2017) [24] & Texture primitive theory & \multirow{2}{*}{Patterned fabric} & \multirow{2}{*}{Surface with defects or not} & \multirow{2}{*}{Superior to other advanced algorithms} \\ \cline{1-1} \cline{3-4}  & [PERSON] et al. (2015) [25] & & & Texture & \\ \cline{1-1}  & [PERSON] et al. (2015) [25] & MRF model & \multirow{2}{*}{Ink-jet printed fabric} & \multirow{2}{*}{defects such as holes, oil drop marks, scratches, etc.} & Can effectively detect defect texture from ink-jet printing fabric \\ \cline{1-1} Model method & [PERSON] et al. (2017) [26] & Fractal model & High-strength steel & \multirow{2}{*}{High-strength steel, defects including offset, cracks, and dents} & \multirow{2}{*}{Average recognition rate: 88.33\%} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Several typical texture feature-based industrial product surface defect detection methods.  
proved effective by experiments, especially for the defect of junction type. Literature [29] designed a 2-step technological process of particle board defect detection by using SVM and color histogram features to complete the detection of defects and using smoothing and threshold technology to complete defect localization.