Steel is an indispensable raw material in the industrial field,and surface defects seriously affect the quality of steel.Traditional steel surface defect detection methods have low accuracy,slow speed,and high labor intensity,which cannot meet actual production needs.In recent years,deep learning technology has developed rapidly,which can fully explore the underlying feature information oftarget images,bringing new solutions to steel defect detection.Summarize the relevant literature on steel surface defect detection methods in recent years,briefly describe the principles and applicability of traditional detection methods,analyze the structure and characteristics of deep learning de-tection models,and summarize some technical difficulties in the current field,and look forward to future development trends.
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