# An Improved MB-LBP Defect Recognition Approach for the Surface of Steel Plates
## 4 Experiment and Analysis of Surface Defects Detection Based on MB-LBP
### Comparison of Experiment Result between MB-LBP and Other Algorithms  
To validate the performance of the MB-LBP, a comparison with several other algorithms was made from the perspective of classification accuracy and time efficiency. The classification accuracy is the percentage of identified defects to the total defects. The time efficiency is the required time to compute the same size image on the same platform. In this paper, the scale invariant feature transform (SIFT), the speeded up robust feature (SURF), the gray-level co-occurrence matrix (GLCM), and convolutional auto encoder-semi-supervised generative adversarial networks (CAE-SGAN) were selected for the comparison. The SIFT [27] algorithm is widely used in the fields of image matching, image recognition, and defect detection. It constructs a Gaussian scale space to describe the local features for the purpose of scale invariability, \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Crack** & **Scratch** & **Indentation** & **Pit** & **Scale** & **total** \\ \hline Training & 318 & 117 & 100 & 30 & 511 & 1076 \\ Testing & 296 & 175 & 174 & 139 & 913 & 1697 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Type composition of surface defect sample sets. Figure 15: Variation of time consumption and recognition accuracy with a change of block size n.  
rotation invariability, and illumination invariability. This algorithm has a certain stability on overcoming the influence of noise and affine. The SURF [28] algorithm is improved based on the SIFT algorithm, which has not computed the Gaussian scale space. On the basis of retaining the main information of the feature points, SURF employs the box filtering technique to simulate the Gaussian scale space to improve the feature extraction speed. As a classic texture feature descriptor, GLCM [29] reflects the comprehensive information of a grayscale image with respect to direction, adjacent interval, and transform amplitude, and is the basis for analyzing the local pattern structure and arrangement rules of the images. The dimension of the feature vector extracted by GLCM is \(256\times 256\), hence the computation amount is too large. Aiming at this problem, the KLPP [30] was adopted to reduce the dimensions of the feature vector. CAE-SGAN [9] is a deep learning method based on convolutional auto encoder (CAE) and semi-supervised generative adversarial networks (SGAN). It first trains a stacked CAE through massive unlabeled data. After CAE is trained, the encoder network of CAE is reserved as the feature extractor and is fed into a softmax layer to form a new classifier. SGAN is introduced for semi-supervised learning to further improve the generalization ability of the new method. Same as the other deep learning methods, this method uses the original defect images as the network input. The surface defect detection system for steel plates is an online real-time detection system, time efficiency is its key indicator. According to experience, for an online detection system, in order to ensure real-time requirements, the maximum time efficiency is 100 ms/image. On this basis, the recognition accuracy of the algorithm is as high as possible. For practicality, the accuracy should be at least 90%. Table 2 shows the comparison of the recognition accuracy and time efficiency between the MB-LBP and other algorithms. The time efficiency of SIFT, SURF, and GLCM speed was separately 583 ms, 142 ms, and 248 ms, which is too slow to meet the requirements of online real-time detection. The time efficiency of LBP was 41 ms, which not only meets the real-time requirements, but is also the fastest algorithm. However, its recognition accuracy was only 89.05%, lower than the minimum practical requirements. The time efficiency of MB-LBP was 63 ms. Although it was slightly slower than the LBP, it still meets the real-time requirement. Additionally, it had the highest recognition accuracy, reaching 94.40%. As for CAE-SGAN, as a deep learning method, it had the highest recognition accuracy of 96.21%.