Pavement Crack Segmentation Algorithm Based on Pulse Coupled Neural Network with Brainstorming Optimization

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.