# Surface Defect Detection Methods for Industrial Products: A Review
## 1 Introduction  
In the industrial production process, due to the deficiencies and limitations of existing technology, working conditions, and other factors, the quality of manufactured products is extremely easily affected. Among them, surface defects are the most intuitive manifestation of product quality being affected. Therefore, in order to ensure the qualification ratio and the reliable quality, product surface defect detection [1, 2] is necessary. \"Defect\" can be generally understood as the absence, imperfection or area compared with the normal sample. The comparison between the normal sample and the defective sample of industrial products is shown in Figure 1. Surface defect detection refers to the detection of scratches, defects, foreign body shielding, color contamination, holes, and other defects on the surface of the sample to be tested, so as to obtain a series of relevant information such as the category, contour, location, and size of surface defects of the sample to be tested [3]. Manual defect detection was once the mainstream method, but this method is low in efficiency; the detection results are easily affected by human subjectivity and cannot meet the requirements of real-time detection. It has been gradually replaced by other methods. At present, some scholars have launched relevant research on surface defect detection, involving the latest methods, applications, key issues, and many other aspects [4]. Literature [5] summarizes the current research status of defect detection techniques such as magnetic particle inspection, penetrant inspection, eddy current inspection, ultrasonic inspection, machine vision, and deep learning [6, 7]; compares and analyzes the advantagesand disadvantages of the above methods; and combs the defect detection technology in electronic components, piping, welding parts, machinery parts, and the typical applications in quality control. From supervised learning model method, unsupervised learning model method [8], and other methods [9] (semi-supervised learning model method and weakly supervised learning model method), literature [10] analyzes surface defect detection methods based on deep learning, and then, three key problems of real-time, small samples, and comparison with traditional image processing-based defect detection methods in surface defect detection are discussed. After reviewing the automatic optical (visual) inspection (AOI) technology, literature [11] systematically described several steps and related methods used in the technology for surface defect detection. Literature [12] first lists the different objects in the field of defects; the mainstream technologies and deep learning methods used for defect detection are introduced and compared. After that, the applications of ultrasonic detection and deep learning methods in defect detection are analyzed. Finally, the existing applications are investigated and based on defect detection equipment, several challenges for defect detection are proposed, such as three-dimensional target detection, high precision, high positioning, rapid detection, small targets, etc. Through investigation, it can be found that in the field of surface defect detection of industrial products, there is currently little literature review on machine learning methods, and although some literatures summarize the problems and challenges in surface defect detection for industrial products, their solutions and directions are not systematic enough. In addition, in terms of datasets, there is still no comprehensive arrangement of industrial product surface defect detection datasets. Therefore, in order to solve the above problems, this paper firstly summarizes the research status of industrial product surface defect detection from the traditional machine vision method and deep learning method, after that, the key problems in the process of industrial surface defect detection, real-time problems, small sample problems, small target problem, unbalanced sample problem, are discussed, and some solutions for each problem are given.