基于视觉显著性的船舶结构缺陷检测研究
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摘要
针对船舶结构缺陷图像中背景结构复杂,缺陷不易检测的难题,提出基于视觉显著性的缺陷检测算法。该算法采用自适应全变分模型替代FT算法的高斯滤波器并融入H分量来构建改进型FT算法,以增强缺陷图像背景的平滑和缺陷局部颜色对比度的表达能力;利用SMD算法捕捉图像结构信息及分解图像特征矩阵,凸显船舶缺陷与背景的差异性;通过自适应融合算法将改进型FT算法和SMD算法生成的显著图融合为综合显著图。对船舶缺陷进行对比实验,结果表明,提出的算法能够有效检测出船舶缺陷区域,提高缺陷检测精确率。
Aiming at the problem that the background structure of ship structure defects is complex and the defects are difficult to detect, a defect detection algorithm based on visual saliency is proposed. The improved FT algorithm replaces the Gaussian filter with an adaptive total variation model, incorporates the H component, and enhances the smoothness of the defect image background and the expression ability of the defect local color contrast. The SMD algorithm is used to capture the image structure information, and the image feature matrix is decomposed to highlight the difference between the ship defect and the background. The explicit map generated by the improved FT algorithm and the SMD algorithm is merged into a comprehensive saliency map by an adaptive fusion algorithm. By comparing the ship's defects, the results show that the proposed algorithm can effectively detect the ship defect area and significantly improve the defect detection precision.
Aiming at the problem that the background structure of ship structure defects is complex and the defects are difficult to detect, a defect detection algorithm based on visual saliency is proposed. The improved FT algorithm replaces the Gaussian filter with an adaptive total variation model, incorporates the H component, and enhances the smoothness of the defect image background and the expression ability of the defect local color contrast. The SMD algorithm is used to capture the image structure information, and the image feature matrix is decomposed to highlight the difference between the ship defect and the background. The explicit map generated by the improved FT algorithm and the SMD algorithm is merged into a comprehensive saliency map by an adaptive fusion algorithm. By comparing the ship's defects, the results show that the proposed algorithm can effectively detect the ship defect area and significantly improve the defect detection precision.
引文
[1]李瑛.船体表面缺陷检测系统研究[J].舰船科学技术,2016(24):109-111.LI Ying.Research on hull surface defect detection system[J].Ship Science and Technology,2016(24):109-111.
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[3]EICH M,BONNINPASCUAL F,GARCIAFIDALGO E,et al.A robot application to marine vessel inspection[J].Journal of Field Robotics,2014,31(2):319-341.
[4]BONNíN-PASCUAL F,ORTIZ A.Detection of Cracks and Corrosion for Automated Vessels Visual Inspection[J].Integrated Pest Management Reviews,2010,3(2):111-120.
[5]BONNIN-PASCUAL F,ORTIZ A.Corrosion Detection for Automated Visual Inspection[M].Austria:In Tech,2014:619-632.
[6]ACHANTA R,HEMAMI S,ESTRADA F,et al.Frequencytuned salient region detection[C].Conference on Computer Vision and Parttern recognition,2009:1597-1604.
[7]PAN Jinquan,CHEN Shuner,FENG Yuanhua,et.al Method of Image Block Denoising Based on Adaptive Total Variation[J].International Journal of Computer Techniques,2016,4(3):174-179.
[8]PENG H,LI B,LING H,et al.Salient Object Detection via Structured Matrix Decomposition[J].IEEE Transactions on Pattern Analysis&Machine Intelligence,2017,39(4):818-832.
[9]ITTI L,KOCH C.Computational modelling of visual attention[J].Nature Reviews Neuroscience,2001,2(3):194-203.
[10]ZHAI Y,SHAH M.Visual Attention detection in video sequences using spatiotemporal cues[C].New York:Proceedings of the14th Annual ACM International Conference on Multimedia,2006:815-824.
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