Surface defect detection is a key aspect of quality inspection of industrial components. Aiming at the lack of systematic literature research on surface defect detection algorithms for industrial parts, China National Knowledge Infrastructure (CNKI) and WOS (Web of Science) core ensemble databases are selected as data sources between 2017 and 2023. With the help of CiteSpace visual analysis software, the research line of surface defect detection algorithms in the field of industrial components inspection is analysed by the number of annual publications and keyword clustering. The current state of research on deep learning-based algorithms for detecting surface defects on industrial components is systematically presented, as well as the practical applications of single-stage and two-stage target detection algorithms. The key problems of current surface defect detection algorithms for industrial components and the corresponding solution strategies are summarized. The future development of surface defect detection algorithms for industrial components is also discussed.