针对现有智能交通系统（ITS）多车牌定位识别算法漏检率高、处理速度慢等问题，在研究细胞神经网络（CNN）理论的基础上，提出了一种基于CNN同心邻域极值（CNE）的ITS图像多车牌区域边缘检测算法，简称CNECNN算法。该算法只需计算CNN中同心邻域内极大值与极小值函数差的二阶微分零交叉点，即可获得图像的边缘。此外，该算法利用CNN稳态能量函数惩罚约束机制优化粒子群适应度函数，在解空间中搜索参数全局最优解以获得CNN邻域极值模板参数。该算法为并行算法，具有运算量小，易于大规模集成电路实现，能够克服早熟收敛等优点。实验结果表明，与传统边缘检测算子和CNN通用机（CNNUM）固定模板参数算法相比，该算法漏检度降低了12.9%。%The cellular neural network (CNN) theory was applied to the study of edge detection in the multi license area of an intelligent transportation system (ITS)’ images to improve the ITS’ performance in license plate recognition, and a new edge detection algorithm based on the concentric neighborhood extreme (CNE) value of CNN, called CNECNN algorithm for short, was put forward. To get the edge area, this algorithm calculates the zero crossing point of a difference function that depends only on the concentric neighborhood extreme value. In order to obtain the CNN template parameters, an energy function constraint method is used to construct a new fitness function of particle swarm optimization (PSO), jumping out the premature convergence, and ultimately find the optimal solution. This new approach can be easily used for VLSI implementation because of its parallelism. Compared with the traditional edge detection operators and general edge detection template in CNN Universal Machine (CNNUM), the simulation results of the images collected in the real environment show that the algorithm based on CNECNN can reduce the miss rate by 12.9%.
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