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The detection of product defects is essential in quality control in manufacturing. This study surveys stateoftheart deep-learning methods in defect detection. First, we classify the defects of products, such as electronic components, pipes, welded parts, and textile materials, into categories. Second, recent mainstream techniques and deep-learning methods for defects are reviewed with their characteristics, strengths, and shortcomings described. Third, we summarize and analyze the applicationdoi:10.3390/ma13245755 pmid:33339413 pmcid:PMC7766692 fatcat:egyrdqjmqvebvoeaqf5yyi3cxm