路面裂缝图像自动识别算法综述

A Review of Automatic Pavement Crack Image Recognition Algorithms

路面裂缝自动检测对于路面养护管理、路面性能评价与预测、路面材料和结构设计具有重要的实用价值,但快速、准确、全面且稳定地识别路面裂缝一直是个难题。为此,对路面裂缝自动检测研究现状进行综述,包括以图像增强和去噪为目的的预处理方法,基于阈值分割、边缘检测和种子生长的空间域识别算法,以小波变换为代表的频域识别算法,基于有监督学习的识别算法及其他裂缝识别方法;指出既有裂缝识别算法存在易受光照和油污等因素的影响、裂缝识别图像连续性差和识别速度和精度较低等不足。最后,提出综合考虑边界和区域特征消除纹理和噪声干扰、基于局部和全局信息设计优化识别算法和基于三维图像进行裂缝识别等研究展望,为裂缝自动识别算法的改进提供参考。

Automatic pavement crack detection is of great practical value for pavement maintenance and management, pavement performance evaluation and prediction, and materials and structure design. However, it remains a difficulty to recognize pavement crack rapidly, precisely, completely and robustly. Thus, the researches on automatic pavement crack detection is reviewed, including the pre-processing methods aiming at image enhancement and denoising, the space-domain recognition algorithms based on thresholding, edge detection and seeds growing, the frequency-domain recognition algorithms such as wavelet transformation, the recognition algorithms based on supervised learning and others. The shortcomings of these crack recognition algorithms are pointed out as follows： （1） lighting and oils tend to impact algorithm performance; （2） crack recognition images have poor continuity; （3） processing speed and recognition precision are not satisfying At last, several research prospects are proposed as references for improvement of pavement recognition algorithms, including ： （ 1 ） remove influences of texture and noises by combining boundary and area features ; （2） design optimization recognition algorithm based on local and global information; （3） recognize pavement crack based on 3D images.