由于受到光照等因素造成的散斑噪声和灰度不均衡现象的影响，应用计算机视 觉技术实现行人的准确检测较为困难． 为了提高交通场景信息提取的精准度和自动化水 平，文中提出一种基于自适应脉冲耦合神经网络的行人检测方法． 首先以像素间“准欧 式”距离为参考，确定神经网络接受区中心神经元与邻域神经元间的点火贡献关系;然后 根据图像灰度特征以及邻域综合信息对脉冲产生区的关键控制参数———初始阈值进行设 定;最后对获得的初始结果进行多策略形态学修正，从而提取出图像中的行人． 实验结果 表明，该方法能够在有效提高检测方法自适应程度的同时，显著去除噪声的影响，较好地 抑制过分割的问题，检测到相对完整的目标．

It is rather difficult to detect pedestrians accurately in the traffic images stained by speckle noise and in- tensity distortions under complex illumination.In order to solve this problem and improve the accuracy and automa- tion level of information extraction from traffic images,a new pedestrian detection method,which is based on adap- tive pulse-coupled neural networks,is proposed.In the investigation,first,the ignition contribution values between the central nerve and its neighborhoods are determined according to the quasi-Euclidean distance between pixels.Then,a key control parameter named initial threshold is set by merging gray feature and neighborhood information.Finally,multi-strategy morphological modifications are performed on the initial detection results to obtain the final pedestrian information.Experimental results demonstrate that the proposed method greatly eliminates the impact of noise,well restrains the over-segmentation,and helps to obtain satisfactory detection results with good adaptability.