基于张量投票和目标追踪的墙体裂缝检测算法研究

针对墙体裂缝对比度低、受噪声干扰严重等特点，提出了一种基于张量投票和目标追踪的墙体裂缝检测算法。在对原始影像进行二值化的基础上，利用张量投票的聚类思想，突出线性区域目标的强度，然后在突出的线性目标上选取特征点，对特征点进行追踪得到裂缝的大致位置，再反投到原始影像上。实验结果表明，该算法能够正确检测出墙体裂缝信息，且检测率较高，具有一定的通用性。

Research on Wall Crack Detection Based on Tensor Voting and Target Tracking

Considering the low contrast and severe noise features of wall crack images, this paper proposes a crack detection algorithm based on tensor voting and target tracking. On the basis of image binarization, the idea of clustering with tensor voting is used to highlight the linear target of the image. Then feature points are chosen on the highlighted linear target, and locations of cracks on the image are obtained through feature points tracking. Finally, the concept of re-cast is utilized to capture the original information of cracks. Test results show that this algorithm can detect cracks correctly on wall images and has high detection rate along with some degree of universality.