AUTOMATIC PAVEMENT CRACK RECOGNITION BASED ON BP NEURAL NETWORK

摘要

现实有效的路面开裂检测系统在路况评价和路面养护中发挥着举足轻重的作用。本研究将BP神经网络(BPNN'back propagation neural network)用于路面开裂图像的自动识别。为了改善BPNN的识别精度,提出了包括图像预处理和开裂信息提取在内的完整的图像处理算法框架。该框架能最大限度地减少图像中的冗余信息,并构造了2组特征向量用于开裂图像的分类。然后利用BPNN进行高精度的线裂图像和龟裂图像的自动识别,而线裂图像又可进一步根据方向角被区分为横裂和纵裂图像。最后,用ARAN (Automatic Road Analyzer)从中国北部采集的400张路面图像对上述方法进行了验证,得到了良好的识别结果。龟裂、横裂和纵裂图像的正确识别率分别达到97.5%、100%和88.0%。与以往的研究相比,该方法能同时有效作用于3类不同开裂,且识别精度能够现实工程应用的需要。%A feasible pavement crack detection system plays an important role in evaluating the road condition and providing the necessary road maintenance. In this paper, a back propagation neural network (BPNN) is used to recognize pavement cracks from images. To improve the recognition accuracy of the BPNN, a complete framework of image processing is proposed including image preprocessing and crack information extraction. In this framework, the redundant image information is reduced as much as possible and two sets of feature parameters are constructed to classify the crack images. Then a BPNN Is adopted to distinguish pavement images between linear and alligator cracks to acquire high recognition accuracy. Besides, the linear cracks can be further classified into transversal and longitudinal cracks according to the direction angle. Finally, the proposed method is evaluated on the data of 400 pavement images obtained by the Automatic Road Analyzer (ARAN) in Northern China and the results show that the proposed method seems to be a powerful tool for pavement crack recognition. The rates of correct classification for alligator, transversal and longitudinal cracks are 97.5%,100% and 88.0%, respectively. Compared to some previous studies, the method proposed in this paper is effective for all three kinds of cracks and the results are also acceptable for engineering application.