基于图像增强与分水岭分割的隧道低对比度裂缝提取方法

摘要

In the process of tunnel crack detection in actual scene,there exists small,low-contrast and stain-interfered cracks.It is difficult to extract those cracks by conventional methods.In order to solve this problem,a crack detection method based on image enhancement and watershed segmentation was proposed.In this method,the interfered stain is removed to balance the image background contrast.The image is further enhanced by top-hat and bottom-hat transfor-mation.Then the segmentation lines are obtained by watershed algorithm.According to the gray-value difference be-tween the gray-value of segmentation line and its surrounding gray-value,the crack edge can be extracted.Experimental results show that the proposed method is accurate and effective to detect tunnel cracks and it is also robust to noise.%在实际的隧道裂缝检测中,存在细小、对比度低且有污渍点干扰的隧道裂缝,利用常规方法很容易漏检裂缝.为了解决此问题,提出一种基于图像增强与分水岭分割的裂缝提取算法,该算法有效利用背景信息补偿了污渍点,均衡了图像背景对比度.结合高低帽变换方法对图像进行增强,然后根据分水岭算法获取分水岭分割线;比较分割线所在位置的灰度值与其周边灰度值,并通过灰度值差异判断裂缝边缘,从而提取裂缝.实验结果表明,所提算法能够准确、有效地检测出完整的隧道裂缝,且对噪声具有鲁棒性.