本文選題：鋼板缺陷檢測 + 不均勻光照矯正　； 參考：《大連海事大學(xué)》2017年碩士論文

【摘要】：鋼材行業(yè)作為國民經(jīng)濟的基礎(chǔ)產(chǎn)業(yè),在經(jīng)濟建設(shè)、社會發(fā)展等多方面都發(fā)揮著重要作用,但鋼板表面缺陷嚴(yán)重降低鋼材質(zhì)量,鋼板表面缺陷檢測是保證鋼材質(zhì)量的關(guān)鍵因素之一。本文對鋼板表面缺陷檢測與分類算法進行了較為深入的研究,具體成果如下:(1)針對鋼板表面圖像數(shù)據(jù)量大、計算效率低的問題,本文構(gòu)造了基于梯度的鋼板表面圖像感興趣區(qū)域檢測算法。通過鋼板表面圖像的局部梯度統(tǒng)計值判斷有無缺陷,該算法可以減小系統(tǒng)后端圖像處理壓力,提升整體效率。(2)針對部分缺陷圖像受不均勻光照影響且對比度低的問題,本文構(gòu)造了基于Retinex算法與導(dǎo)向濾波器的不均勻光照矯正和增強算法。利用導(dǎo)向濾波器估計光照分量,根據(jù)Retinex算法計算反射分量,并對反射分量進行增強,提高對比度,恢復(fù)原始灰度信息。(3)針對缺陷分割準(zhǔn)確度問題,本文構(gòu)造了基于Center-Surround Difference的缺陷圖像分割算法。利用帶權(quán)重的組合DoG(Difference of Gaussian)濾波器對預(yù)處理后缺陷圖像進行濾波,根據(jù)Center-Surround Difference提取圖像Local特征和Global特征,并將兩者線性融合,對融合后圖像進行背景抑制和前景恢復(fù),利用自適應(yīng)閾值實現(xiàn)缺陷區(qū)域提取。(4)根據(jù)缺陷圖像分割前后所含的不同信息,提取36維特征值作為缺陷分類依據(jù)。針對缺陷分類精度問題,從慣性權(quán)值和種群多樣性兩個方面對粒子群(Particle Swarm Optimization,PSO)算法進行改進,利用改進算法對前饋(Back Propagation,BP)神經(jīng)網(wǎng)絡(luò)進行優(yōu)化,最終實現(xiàn)缺陷分類。實驗結(jié)果表明,本文設(shè)計的算法能很好的實現(xiàn)缺陷的檢測和分類識別,實驗主觀效果和客觀效果保持一致。
[Abstract]:As the basic industry of the national economy, the steel industry plays an important role in economic construction, social development and other aspects, but the steel plate surface defects seriously reduce the quality of steel, Surface defect detection of steel plate is one of the key factors to ensure steel quality. In this paper, the surface defect detection and classification algorithm of steel plate is studied in depth. The concrete results are as follows: (1) aiming at the problem of large amount of image data and low computational efficiency of steel plate surface, In this paper, a gradient based region of interest detection algorithm for steel plate surface images is proposed. Based on the local gradient statistical value of the steel plate surface image, the algorithm can reduce the image processing pressure and improve the overall efficiency of image processing, aiming at the problem that some defective images are affected by uneven illumination and the contrast is low. In this paper, a nonuniform illumination correction and enhancement algorithm based on Retinex algorithm and guide filter is constructed. The illumination component is estimated by the guide filter, the reflection component is calculated according to the Retinex algorithm, and the reflection component is enhanced, the contrast is improved, the original gray level information is restored. A defect image segmentation algorithm based on Center-Surround Difference is proposed in this paper. A combined DoG(Difference of Gaussian filter with weights is used to filter the pre-processed defective image. The Local and Global features of the image are extracted according to Center-Surround Difference, and the two features are linearly fused to suppress the background and restore the foreground of the fused image. Based on the different information before and after the defect image segmentation, 36 dimensional eigenvalues are extracted as the basis of defect classification. Aiming at the accuracy of defect classification, the particle swarm optimization (PSOs) algorithm is improved from the aspects of inertia weight and population diversity. The improved algorithm is used to optimize the feedforward back Propagation (BP) neural network, and finally the defect classification is realized. The experimental results show that the algorithm designed in this paper can achieve the defect detection and classification, and the subjective and objective effects of the experiment are consistent.
【學(xué)位授予單位】：大連海事大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41

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