# Defect Detection Methods for Industrial Products Using Deep Learning Techniques: A Review
## 2 Introduction
In the texture-based feature method, for example in [10], a multi-block local binary pattern (LBP) algorithm has been improved. In addition to having the simplicity and efficiency of LBP, this algorithm ensures high recognition accuracy by varying the block size to describe defect features. According to the experiment, the method has the speed to meet online real-time detection requirements (63 milliseconds/image), outperform the widely used scale-invariant feature transform (SIFT), speed up robust features (SURF), and gray-level co-occurrence matrix (GLCM) algorithms for recognition accuracy (94.30%), demonstrating that MB-LBP can be used to detect images in real time online. Literature [11] used a fuzzy model that was based on extracting GLCM features and processed it using MATLAB. The model took in three variables as inputs: autocorrelation, square root of variance, and the number of objects. Using fuzzy logic on ceramic defects, the accuracy of the ceramic inspection process with a light intensity of 300 lx, camera distance of 50 cm, and a 1.3 MP or 640 \(\times\) 480 pixel image size was determined using the training data of 96.87%, and the accuracy of the real-time system was 92.31%. According to literature [12], features such as Reduced Coordinated Cluster Representation (RCCR) are used to form a one-class classifier. An algorithm based on texture periodicity estimates the primitive unit size of defect-free fabrics during the training phase. After splitting the fabrics into samples of one unit, RCCR features are used in a one-class classifier to learn their local structure. In [13], morphological filters are used to detect defects on billet surfaces in order to distinguish them from scales. With the help of morphological erosion and dilation techniques with repetition, the image is converted into a binary image by using morphological top-hat filtering and thresholding. The detection efficiency of the proposed algorithm is evaluated using real billet images to evaluate its performance. The proposed algorithm is found to be effective and suitable for analyzing billet images with scales in experiments. According to literature [14], the GLCM is defined as the fabric image's characteristic matrix. To distinguish defect-free from defective images, Euclidean distance is used and, in order to determine the pattern period, the autocorrelation function is used. In this paper, the authors discussed two GLCM parameters in relation to Euclidean distances. Furthermore, in addition to being concise and objective, Euclidean distances have the advantage of being reliable and objective for defect detection. According to the algorithm's tests, it is not only accurate, but also more adaptable to yarn-dyed fabrics with short organization cycles. Table 1 summarizes recent applications of machine learning algorithms for surface defect detection in industrial products, categorized by texture, color, and shape features. Table 2 compares the strengths and weaknesses of feature-based methods for detecting surface defects, including accuracy, computational efficiency, and robustness. These tables provide an overview of the diversity of approaches and key factors affecting performance in the field of surface defect detection. Figure 3: A camera lens with several defects: (**a**) original image and (**b**) converted result based on inspection result and polar coordinate transformation [9].