基于深度學(xué)習(xí)的表面缺陷檢測算法文獻研究

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中圖分類號：TP391文獻標(biāo)識碼：A

Literature Research on Deep Learning-based Algorithms for Surface Defect Detection

HUANG Keteng1，WANG Yuqi1，WANG Qing1， JU Junwei2，BAI Shuowei1 （1. 266O71，China; 2. （Shong） ）

Abstract： Surface defect detection is a key aspect quality inspection industrial components. Aiming at the lack systematic literature research on surface defect detection algorithms for industrial parts，China National Knowledge Infrastructure （CNKI） WOS（Web Science） core ensemble databases are selected as data sources between 2Ol7 2O23. With the help CiteSpace visual analysis stware，the research line surface defect detection algorithms in the field industrial components inspection is analysed by the number annual publications keyword clustering. The current state research on deep learning-based algorithms for detecting surface defects on industrial components is systematically presented，as well as the practical applications single-stage two-stage target detection algorithms. The key problems current surface defect detection algorithms for industrial components the corresponding solution strategies are summarized. The future development surface defect detection algorithms for in

dustrial components is also discussed.

Keywords： deep learning; surface defect detection; industrial components; visualization analysis; CiteSpace

在工業(yè)零件的生產(chǎn)過程中，由于加工工藝、生產(chǎn)原材料或生產(chǎn)環(huán)境等多因素的影響，零件表面會出現(xiàn)劃痕、壓傷、黑皮以及凸起等缺陷[]，不僅影響零件的整體性能和質(zhì)量，還對其使用壽命造成不可逆的損害，甚至可能引發(fā)生產(chǎn)事故，給企業(yè)帶來難以估量的損失。（剩余13554字）