發(fā)布時(shí)間：2018-10-30 11:28

【摘要】：針對(duì)傳統(tǒng)帶鋼表面缺陷檢測(cè)方法在實(shí)際生產(chǎn)過(guò)程中檢測(cè)精度低、實(shí)時(shí)性差的問(wèn)題,提出一種基于復(fù)合差分進(jìn)化的Gabor濾波器優(yōu)化方法.首先,將采集到的圖像進(jìn)行預(yù)處理,獲取高質(zhì)量圖像;然后,針對(duì)傳統(tǒng)Gabor小波濾波器參數(shù)較多和算法實(shí)時(shí)性不高兩大難題,提出了一種復(fù)合差分進(jìn)化的Gabor濾波器優(yōu)化方法,對(duì)參數(shù)和方向分別做了改進(jìn),較大提升了檢測(cè)效率;最后,對(duì)顯著性缺陷目標(biāo)進(jìn)行閾值分割,完成帶鋼表面缺陷檢測(cè).實(shí)驗(yàn)結(jié)果表明:該優(yōu)化算法復(fù)雜度低、檢測(cè)效率高,優(yōu)化后的Gabor檢測(cè)模型在速度上比傳統(tǒng)Gabor檢測(cè)模型快了約2.3倍,平均速度達(dá)到了91.8ms/幀.
[Abstract]:Aiming at the problems of low detection precision and poor real-time performance of traditional strip surface defect detection method, a Gabor filter optimization method based on compound differential evolution is proposed. Firstly, the collected images are preprocessed to obtain high quality images. Then, aiming at the traditional Gabor wavelet filter with many parameters and low real-time algorithm, a compound differential evolution optimization method for Gabor filter is proposed, which improves the parameters and direction of the filter, and improves the detection efficiency greatly. Finally, the significant defect target is segmented by threshold, and the strip surface defect detection is completed. The experimental results show that the proposed algorithm is of low complexity and high detection efficiency. The speed of the optimized Gabor detection model is about 2.3 times faster than that of the traditional Gabor detection model, and the average speed reaches the 91.8ms/ frame.
【作者單位】： 河北工業(yè)大學(xué)控制科學(xué)與工程學(xué)院;河北科技大學(xué)電氣工程學(xué)院;
【基金】：國(guó)家自然科學(xué)基金資助項(xiàng)目(61403119) 河北省自然科學(xué)基金資助項(xiàng)目(F2014202166) 天津市特派員科技計(jì)劃資助項(xiàng)目(15JCTPJC55500)
【分類號(hào)】：TG142.15;TP391.41

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本文編號(hào)：2299874