局部特征聚类联合区域增长的桥梁裂缝检测

针对传统裂缝检测算法抗干扰能力弱,浅层裂缝易误判等问题,提出一种局部特征聚类联合区域增长的桥梁裂缝检测算法。首先,针对混凝土表皮脱落及渗水等干扰问题,采用Gauss-Frangi双重滤波对图像模糊化处理,退化噪声的特征信息,并增强图像中的线性结构。其次,针对常规算法无法识别弱特征的浅层裂缝问题,根据局部区域裂缝点间的空间相关性,提出基于网格聚类联合区域增长算法实现局部区域裂缝的动态分割。最后,针对分割图像中伪裂缝等顽固噪声,提出一种基于形状特征及结构相似性原理方法剔除噪声。实验表明,所提算法可检测出更多的裂缝细节信息,且保持较高的精确率,提高了裂缝图像分割质量。

Bridge Crack Detection Based on Local Feature Clustering Combined with Regional Growth

Aiming at the problems such as weak anti-interference ability of traditional crack detection algorithm and easy misjudgment of shallow cracks, a local feature clustering combined with area growth algorithm for bridge crack detection is proposed. Firstly, in order to solve the problem of crack segmentation affected by the concrete cuticle shedding and water seepage, the Gauss-Frangi dual filter was used to blur the image, degrade the characteristic information of noise, and enhance the linear structure in the image. Secondly, aiming at the problem of shallow cracks with weak features that cannot be recognized by conventional algorithms, according to the spatial correlation between crack points in local regions, a new algorithm based on grid clustering combined with regional growth algorithm was proposed to realize the dynamic segmentation of local regional cracks. Finally, a method based on shape feature and structure similarity principle is proposed to eliminate the pseudo-crack noises in the image segmentation. Experiments show that the proposed algorithm can detect more details of cracks, maintain a high accuracy rate, and improve the quality of crack image segmentation.