本文選題：特征選擇 + 帶鋼表面缺陷　； 參考：《電子測量與儀器學報》2017年07期

【摘要】：帶鋼表面缺陷紋理的復(fù)雜性和多樣性、背景紋理中存在的偽缺陷等給現(xiàn)有的帶鋼表面缺陷特征提取和識別帶來了極大的困難。為此,提出了一種新的帶鋼表面缺陷選擇與識別方法。首先,通過各向異性擴散算法對帶鋼表面的偽缺陷干擾進行抑制;其次,利用提出的P-Relief F方法對表面缺陷特征進行選擇,相比傳統(tǒng)的Relief F方法,該方法考慮了不同維度特征之間的關(guān)聯(lián)性;最后,利用篩選的特征集和支持向量機(SVM)核分類器對帶鋼表面缺陷進行分類與識別。實驗結(jié)果表明,提出的方法能夠提取出具有高區(qū)分性和魯棒性的帶鋼表面缺陷特征,并且對于劃痕、褶皺、凸起和污漬等不同類型的帶鋼表面缺陷,本方法相比傳統(tǒng)的方法可以獲得更高的識別率。
[Abstract]:The complexity and diversity of strip surface defect texture and the existence of pseudo-defects in background texture make it difficult to extract and identify the existing surface defect features of strip steel. Therefore, a new method for selecting and identifying the surface defects of steel strip is proposed. Firstly, the anisotropic diffusion algorithm is used to suppress the pseudo-defect interference on the strip surface. Secondly, the proposed P-Relief F method is used to select the surface defect characteristics, which is compared with the traditional Relief F method. The correlation between different dimension features is considered in this method. Finally, the surface defects of steel strip are classified and identified using the filtered feature set and support vector machine (SVM) kernel classifier. The experimental results show that the proposed method can extract the characteristics of strip surface defects with high discrimination and robustness, and for different types of strip surface defects, such as scratches, folds, bulges and stains, etc. Compared with the traditional method, this method can obtain higher recognition rate.
【作者單位】： 河北工業(yè)大學控制科學與工程學院;河北科技大學電氣工程學院;
【基金】：國家自然科學基金(61403119) 河北省自然科學基金(F2014202166) 天津市特派員科技計劃(15JCTPJC55500)資助項目
【分類號】：TG335.56;TP391.41
，

本文編號：1788368