In semiconductor, printed circuit board (PCB), automobile assembly, liquid crystal display (LCD), 3C, photovoltaic cell, and textile industries, the appearance of the product is closely related to the performance of the product. Surface defect detection is an important way to prevent defective products from entering the market. The utilization of machine vision technology to perform inspections with high efficiency and low cost is the main direction of future development. This article reviews the research progress of surface defect detection methods based on machine vision in recent ten years. Firstly, the definition of defect is given, and the general steps of defect detection are described. Then, it focuses on the principle of defect detection using traditional image processing methods, machine learning, and deep learning. The advantages and disadvantages are compared and analyzed. The traditional image processing methods are divided into segmentation and feature extraction. Machine learning consists of unsupervised learning and supervised learning. Deep learning mainly covers most of the mainstream networks for detection, segmentation and classification. Then, 30 kinds of industrial defect data sets and performance evaluation indexes are introduced. Finally, the existing problems of defect detection methods are pointed out and the further work is prospected.