# Defect Detection Methods for Industrial Products Using Deep Learning Techniques: A Review
## 2 Introduction  
Several factors affect the quality of manufactured products during the manufacturing process, including poor working conditions, inadequate technology, and various other factors. Among product defects, poor product quality is most visible in surface defects. Therefore, detecting product surface defects [1] ensures a high qualification ratio and reliable quality. A defect is generally defined as an absence or area that differs from a normal sample. Figure 1 compares normal samples with defective samples of industrial products. Figure 1: Normal samples of industrial products are compared to defective samples. The first row contains good samples, and the second, third, and fourth rows contain defective samples. The first, second, third, fourth, and fifth columns display wood, grid, capsule, leather, and bill, respectively, and there are three types of defects listed below the image. In the past, identifying defects was carried out by experts, but this process was not efficient. One major reason for this was that human subjectivity greatly affected the detection results. Additionally, human inspection alone cannot meet the need for real-time detection, and thus, it is not able to fulfill all the necessary requirements. A significant amount of time has been dedicated to using traditional methods to detect surface defects. When differentiation exists between the defect color and the background, traditional image processing methods can perform well. Traditional methods in terms of the product's features can be categorized into three types: texture-based features, color-based features, and shape-based features. Several studies have used specialized techniques for detecting surface defects. In color-based feature, for instance, literature [2] proposed a technique involving the use of a percentage of the feature of color histogram and a vector texture feature to classify image blocks to detect surface defects on wood; this method has been proven effective by experiments, especially with defects involving junctions. In Figure 2, the method results are shown. Research conducted in literature [3] employed cosine similarity to verify the validity of the periodic law in magneto-optical images by utilizing the color moment feature. This method was successful in identifying the appropriate magneto-optical image for detecting and locating welding defects. Literature [4] describes a two-step technological process for SVM-based and color histogram-based defect detection in particle boards, followed by localization of defects using smoothing and thresholding. According to literature [5], color moment features and FSIFT features were merged based on their magnitude of influence for the purpose of resolving the tile surface defect problem not being adequately described by a single feature. In terms of shape-based feature methods, literature [6] proposed a method of detecting cutting defects on magnetic surfaces. In this method, the image of the magnetic surface is reconstructed using the Fourier transform and Hough transform, and, in order to obtain defect information, the gray difference between the original image and the reconstructed image is compared. A method for identifying defects on bottle surfaces was presented in reference [7]. This method includes a step for extracting regions of interest, where the boundary line of the light source is determined using a fast Hough transform algorithm. In [8], global Fourier image reconstruction and template matching were proposed as a method for detecting and locating small defects in aperiodic images. Literature [9] described how to detect surface defects on small camera lenses using Hough transforms, polar coordinate transforms, weighted Sobel filters, and SVM algorithms. Different types of defects were detected in several test images. In Figure 3, red highlights are used to indicate defects such as stains, scratches, and dots. Figure 2: An example of the result of wood defect detection using the presented technique in [2].