基于小波神经网络的路面破损识别

Preliminary Study of Pavement Surface Distress Automation Recognition Based on Wavelet Neural Network

结合图象处理、模式识别等先进技术开发路面破损自动检测系统已经成为本领域的研究热点[1-5]。本文主要研究了小波神经网络在路面破损识别中的应用,并与传统的BP神经网络作了对比。试验结果表明,在相同的训练样本情况下,小波神经网络的精度高于BP神经网络。为开发更为高效的路面破损自动检测系统提供新的思路。

Automatic pavement surface distress survey system based on image processing and pattern recognition has become the hotspot in its field[15]. In order to improve the accuracy and efficiency to identify the asphalt pavement surface distress by the image information, wavelet neural network (WNN) is put forward to classify sub-image which is made up of 40 40 pixels of a pavement surface image. Compare between pattern classifier based on traditional BP artificial neural network and that based on WNN was carried, which proved later one is better than the previous one when other conditions are the same, which provide a new method to exploit more efficient automatic pavement surface distress survey system.