Pavement Crack Segmentation Algorithm Based on Pulse Coupled Neural Network with Brainstorming Optimization
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摘要: 为提升裂缝检测的分割精度和鲁棒性，基于头脑风暴优化（brainstorming optimization，BSO）和脉冲耦合神经网络（pulse coupled neural network，PCNN），提出了一种路面裂缝图像分割算法（BSO-PCNN）. 该算法采用最大熵准则作为BSO算法的适应度函数，并依据适应度值决定参与次轮迭代的个体；BSO具有强收敛性，可快速确定最优个体解；结合图像特征，获得PCNN模型的最优参数，将其代入PCNN模型实现对裂缝图像的分割. 试验结果表明：算法可在20次迭代内取得不同类型路面裂缝图像的最大适应值，从而确定最佳分割参数；与Sobel边缘检测算法、PCNN图像分割算法、基于最大熵的遗传算法（genetic algorithm based on the maximun entropy of the histogram，GA-KSW）、基于遗传算法参数优化的PCNN分割算法（genetic algorithm based on the pulse coupled neural network，GA-PCNN）相比，BSO-PCNN算法取得了0.9924的区域一致性与0.0900的区域对比度.Abstract: In order to improve the segmentation accuracy and robustness of crack detection, a new segmentation algorithm for pavement crack image is proposed on the basis of brain storming optimization (BSO) and pulse coupled neural network (PCNN). This method uses the maximum entropy criterion as the fitness function of the BSO algorithm, and then determines the individuals participating in the next iteration according to the fitness value. Since the BSO algorithm has strong convergence, which is able to quickly determine the optimal individual solution. Combining the image features, the optimal parameters of the model are obtained, which can be substituted into the PCNN model to achieve the segmentation of the crack image. The experimental results show that the maximum fitness value of different road crack images can be obtained within 20 iterations, so as to determine the best segmentation parameters. Compared with traditional crack segmentation algorithms like Sobel, PCNN, genetic algorithm based on the maximun entropy of the histogram (GA-KSW), and genetic algorithm based on the pulse coupled neural network (GA-PCNN), the proposed method achieves a regional consistency accuracy of 0.9924 and a regional contrast of 0.9924 and a regional contrast of 0.0900.
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Key words:
- road engineering /
- cracks /
- image segmentation /
- pulse coupled neural network (PCNN) /
- brainstorming
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表 1 不同算法对比试验
Table 1. Results Experiments of different algorithms comparison
图像编号 原图 Sobel PCNN GA-KSW GA-PCNN BSO-PCNN 1 
2 
3 
4 
5 
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表 2 不同算法的区域对比度和区域一致性
Table 2. Regional contrast and consistency of different algorithms
图像 区域对比度 区域一致性 Sobel PCNN GA-KSW GA-PCNN BSO-PCNN Sobel PCNN GA-KSW GA-PCNN BSO-PCNN 1 0.0487 0.1967 0.0866 0.0736 0.1295 0.9904 0.9909 0.9907 0.9906 0.9909 2 0.0344 0.0719 0.0887 0.0950 0.0912 0.9878 0.9882 0.9885 0.9885 0.9886 3 0.0124 0.0236 0.0513 0.0354 0.0642 0.9962 0.9963 0.9965 0.9964 0.9965 4 0.0814 0.0578 0.1040 0.0938 0.1100 0.9900 0.9898 0.9913 0.9910 0.9913 5 0.0218 0.0375 0.0500 0.0535 0.0538 0.9943 0.9946 0.9947 0.9945 0.9947 平均值 0.0397 0.0775 0.0761 0.0703 0.0900 0.9917 0.9920 0.9923 0.9922 0.9924
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表 3 算法时间和信噪比比较
Table 3. Comparison of algorithm time and signal-to-noise ratio
图像 算法运行时间/s 信噪比/dB Sobel PCNN GA-KSW GA-PCNN BSO-PCNN Sobel PCNN GA-KSW GA-PCNN BSO-PCNN 1 0.596 7.483 0.413 3.289 3.147 2.3408 2.2683 3.0673 1.6554 3.4219 2 0.702 7.378 0.018 3.215 3.017 1.4490 1.9176 3.5000 4.6284 3.4389 3 0.675 7.424 0.023 3.315 3.046 1.8633 1.6814 3.7504 1.4826 3.8259 4 0.648 7.480 0.028 3.253 2.981 0.7415 0.0920 0.1070 0.0578 0.2767 5 0.682 7.632 0.019 3.251 3.003 0.8994 0.0545 1.2121 5.9184 2.6544 平均值 0.661 7.479 0.100 3.265 3.039 1.4588 1.2028 2.3274 2.7489 2.7236
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