多因子判定与渗流模型相结合的裂缝检测算法
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摘要
针对传统的基于渗流模型的裂缝检测算法效率过低且检测结果易存在断裂的问题,提出一种多因子判定与渗流模型相结合的裂缝检测算法。首先,提出了一种改进的渗流加速算法,通过减少大量参与渗流处理的冗余像素点,提高渗流处理效率;然后,对提取到的渗流点进行渗流处理;最后,提出了一种结合裂缝走向的多因子判定连接算法,算法通过四个判定因子对裂缝连接的合理性进行分析,以提高裂缝连接的准确性。对背景中存在不同干扰物的不同形态裂缝图像进行实验,与传统渗流模型检测算法以及原渗流加速-骨架连接算法相比,所提算法中渗流点数量分别平均减少了99. 7%与38. 1%,精确率分别平均提高了60. 5%与6. 4%,召回率分别平均提高了10. 5%与4. 0%。实验结果表明,所提算法能够明显提高渗流处理效率,同时提高裂缝检测的准确性。
Concerning the problem that traditional crack detection algorithm based on percolation model has low efficiency and detection results are prone to fracture, a crack detection algorithm based on multi-factor decision and percolation model was proposed. Firstly, an improved algorithm of accelerating crack inspection based on percolation model was proposed, which improves the efficiency of percolation processing by reducing a large number of redundant pixel points involved in percolation processing. Secondly, the extracted percolation points were used to percolation processing. Finally, a multi-factor decision connection algorithm based on crack orientation was proposed. In the algorithm, the rationality of crack connection was analyzed by four decision factors to improve the accuracy of crack connection. Different morphological crack images with different interfering objects in background were used in experiments. Compared with traditional percolation model detection algorithm and original algorithm of accelerating crack inspection based on percolation model and skeleton connection algorithm,the number of percolation points of the proposed algorithm was reduced by an average of 99. 7% and 38. 1%, respectively.The precision was increased by an average of 60. 5% and 6. 4%, respectively, and the recall was increased by an average of10. 5% and 4. 0%, respectively. The experimental results show that the proposed algorithm can significantly improve the efficiency of percolation processing and improve the accuracy of crack detection.
Concerning the problem that traditional crack detection algorithm based on percolation model has low efficiency and detection results are prone to fracture, a crack detection algorithm based on multi-factor decision and percolation model was proposed. Firstly, an improved algorithm of accelerating crack inspection based on percolation model was proposed, which improves the efficiency of percolation processing by reducing a large number of redundant pixel points involved in percolation processing. Secondly, the extracted percolation points were used to percolation processing. Finally, a multi-factor decision connection algorithm based on crack orientation was proposed. In the algorithm, the rationality of crack connection was analyzed by four decision factors to improve the accuracy of crack connection. Different morphological crack images with different interfering objects in background were used in experiments. Compared with traditional percolation model detection algorithm and original algorithm of accelerating crack inspection based on percolation model and skeleton connection algorithm,the number of percolation points of the proposed algorithm was reduced by an average of 99. 7% and 38. 1%, respectively.The precision was increased by an average of 60. 5% and 6. 4%, respectively, and the recall was increased by an average of10. 5% and 4. 0%, respectively. The experimental results show that the proposed algorithm can significantly improve the efficiency of percolation processing and improve the accuracy of crack detection.
引文
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[10]YAMAGUCHI T, HASHIMOTO S. Automated crack detection for concrete surface image using percolation model and edge information[C]//Proceedings of the 2006 IEEE Conference on Industrial Electronics. Piscataway, NJ:IEEE, 2006:3355-3360.
[11]YAMAGUCHI T, HASHIMOTO S. Fast crack detection method for large-size concrete surface images using percolation-based image processing[J]. Machine Vision and Applications, 2010, 21(5):797-809.
[12]QU Z, GUO Y, JU F R, et al. The algorithm of accelerated cracks detection and extracting skeleton by direction chain code in concrete surface image[J]. The Imaging Science Journal, 2016, 64(3):119-130.
[13]瞿中,郭阳,鞠芳蓉.一种基于改进渗流模型的混凝土表面裂缝快速检测算法[J].计算机科学,2017,44(1):300-302.(QU Z,GUO Y, JU F R. Algorithm of accelerated cracks detection based on improved percolation model in concrete surface image[J]. Computer Science, 2017, 44(1):300-302.)
[14]瞿中,蒋玉萍,文倩云.一种方向链码扫描与跟踪的图像细化后期处理算法[J].计算机科学,2015,42(2):292-295.(QU Z,JIANG Y P, WEN Q Y. Algorithm of image thinning post-processing based on direction chain code scanning and tracking[J]. Computer Science, 2015, 42(2):292-295.)
[15]吴成东,卢佰华,陈东岳,等.基于方向特征及引力模型的路面裂缝检测[J].东北大学学报(自然科学版),2012,33(4):469-472.(WU C D, LU B H, CHEN D Y, et al. Pavement crack detection based on direction feature and gravitational model[J].Journal of Northeastern University(Natural Science), 2012, 33(4):469-472.)
[2]王军,孙慧婷,姜志,等.基于Hessian矩阵多尺度滤波的路面裂缝图像检测方法[J].计算机应用,2016,36(S1):174-176.(WANG J, SUN H T, JIANG Z, et al. Pavement crack detection based on Hessian matrix multi-scale filtering[J]. Journal of Computer Applications, 2016, 36(S1):174-176.)
[3]ALDEA E, LE HGARAT-MASCLE S. Robust crack detection for unmanned aerial vehicles inspection in an a-contrario decision framework[J]. Journal of Electronic Imaging, 2015, 24(6):061119.
[4]SUN L, KAMALIARDAKANI M, ZHANG Y M. Weighted neighborhood pixels segmentation method for automated detection of cracks on pavement surface images[J]. Journal of Computing in Civil Engineering, 2016, 30(2):04015021.
[5]任亮,徐志刚,赵祥模,等.基于Prim最小生成树的路面裂缝连接算法[J].计算机工程,2015,41(1):31-36.(REN L, XU Z G,ZHAO X M, et al. Pavement crack connection algorithm based on Prim minimum spanning tree[J]. Computer Engineering, 2015, 41(1):31-36.)
[6]赵珊珊,何宁.基于卷积神经网络的路面裂缝检测[J].传感器与微系统,2017,36(11):135-138.(ZHAO S S, HE N. Pavement crack detection based on convolution neural network[J]. Transducer and Microsystem Technologies, 2017, 36(11):135-138.)
[7]AMHAZ R, CHAMBON S, IDIER J, et al. Automatic crack detection on two-dimensional pavement images:an algorithm based on minimal path selection[J]. IEEE Transactions on Intelligent Transportation Systems, 2016, 17(10):2718-2729.
[8]宰柯楠,徐江峰.基于遗传算法和简化PCNN的裂缝检测方法[J].计算机应用研究,2017,34(6):1885-1888.(ZAI K N, XU J F. Method of crack detection based on genetic algorithm and simplified pulse coupled neural network[J]. Application Research of Computers, 2017, 34(6):1885-1888.)
[9]朱平哲,黎蔚.基于主动生长的断裂裂缝块的连接方法[J].计算机应用,2011,31(12):3382-3384.(ZHU P Z, LI W. Linking algorithm of discontinuity crack block based on autonomous edge growing[J]. Journal of Computer Applications, 2011, 31(12):3382-3384.)
[10]YAMAGUCHI T, HASHIMOTO S. Automated crack detection for concrete surface image using percolation model and edge information[C]//Proceedings of the 2006 IEEE Conference on Industrial Electronics. Piscataway, NJ:IEEE, 2006:3355-3360.
[11]YAMAGUCHI T, HASHIMOTO S. Fast crack detection method for large-size concrete surface images using percolation-based image processing[J]. Machine Vision and Applications, 2010, 21(5):797-809.
[12]QU Z, GUO Y, JU F R, et al. The algorithm of accelerated cracks detection and extracting skeleton by direction chain code in concrete surface image[J]. The Imaging Science Journal, 2016, 64(3):119-130.
[13]瞿中,郭阳,鞠芳蓉.一种基于改进渗流模型的混凝土表面裂缝快速检测算法[J].计算机科学,2017,44(1):300-302.(QU Z,GUO Y, JU F R. Algorithm of accelerated cracks detection based on improved percolation model in concrete surface image[J]. Computer Science, 2017, 44(1):300-302.)
[14]瞿中,蒋玉萍,文倩云.一种方向链码扫描与跟踪的图像细化后期处理算法[J].计算机科学,2015,42(2):292-295.(QU Z,JIANG Y P, WEN Q Y. Algorithm of image thinning post-processing based on direction chain code scanning and tracking[J]. Computer Science, 2015, 42(2):292-295.)
[15]吴成东,卢佰华,陈东岳,等.基于方向特征及引力模型的路面裂缝检测[J].东北大学学报(自然科学版),2012,33(4):469-472.(WU C D, LU B H, CHEN D Y, et al. Pavement crack detection based on direction feature and gravitational model[J].Journal of Northeastern University(Natural Science), 2012, 33(4):469-472.)