裂缝检测是结构安全性评估的重要指标，基于图像处理的传统裂缝检测方法在光照不均匀、噪声污染严重的隧道环境下具有噪声大、精度低等缺点。针对该问题，提出一种基于机器视觉的隧道裂缝识别算法，首先对隧道图像进行频域滤波与空域差分，增强图像纹理特征；将经上述步骤分割后的图像通过设置面积参数Tv、饱和度参数Ts与特殊参数Tv`、Ts`提取背景噪声并删除，使算法能够检测出完整的隧道裂缝图像；最后，结合本文应用场景的无突变性与发展规律性，设计轻量化裂缝连接算法连接上述步骤中断裂的裂缝，避免漏检现象的发生。实验结果表明，本文方法能够在复杂隧道环境有效提取出完整裂缝，对隧道裂缝图像识别精确率达到94%，召回率达到98%，尺寸精度达到92%。检测精度能够满足实际工程需求。

Crack detection is an important indicator of structural safety assessment. Traditional crack detection methods based on image processing have the disadvantages of large noise and low accuracy in a tunnel environment with uneven illumination and serious noise pollution. Aiming at this problem, a tunnel crack recognition algorithm based on machine vision is proposed. First, the tunnel image is filtered in frequency domain and is differenced in spatial domain to enhance the image texture features; then the image segmented by the above steps is extracted by setting the area parameter Tv, the saturation parameter Ts and the special parameters Tv`, Ts` to extract the background noises and delete them, in order to detect the complete tunnel crack images; finally, combining the non-mutation and development regularity of the application scenario in this paper, designing a lightweight crack connection algorithm to connect the breakpoints in crack images to avoid the undetected phenomenon. Experiments show that the images processed in this paper can effectively extract complete cracks, and the accuracy of image recognition of tunnel cracks reaches 94%, the recall rate reaches 98%, and the detection accuracy can meet the actual engineering needs.

TP391.4???

甘肃省自然科学基金资助项目