公路隧道视频预处理和病害识别算法

基于计算机视觉技术,针对公路隧道病害进行检测与识别,提出视频数据的预处理方法.使用全卷积网络(FCN)模型识别病害的类别和位置,融合不同的上采样结果使最终结果更加精细,结合马尔可夫随机场(MRF)增强FCN模型的空间一致性.实验结果表明:该方法可解决数据冗余、镜头畸变及样本不均衡等问题;该方法在上海市虹梅南路隧道中的应用结果验证其准确度与可靠性.

Video Preprocess and Defect Recognition Algorithm for Road Tunnel

Based on computer vision, the defect detection and identification were conducted for road tunnel. A preprocess method for video data was proposed. Finally, fully convolutional networks(FCN)model was used to identify the category and location of defects. Different up-sampling results were integrated to make the final results more precisely, and the spatial consistency of FCN model was improved by combining Markov random field(MRF). The experimental results show that this method can solve the problems of data redundancy, lens distortion and sample imbalance. The application of this method in the Shanghai Hongmei South Road Tunnel in Shanghai validates its accuracy and reliability.