高分辨率桥梁裂缝图像实时检测

Real Time Detection of High Resolution Bridge Crack Image

针对现有桥梁裂缝检测算法实时性弱、可靠性差等问题,提出一种嵌入式平台上的实时检测算法。使用移动平均法粗分割,依据几何特征和区域生长法再分割,定位候选裂缝片段;基于裂缝先验条件,建立双判别准则的裂缝聚合模型,递归合并裂缝片段并抑制干扰。实验表明,算法能有效提取细小裂缝,抑制不均匀光照和污渍等复杂背景干扰,识别性能与数种现有算法相比提高115%以上;在嵌入式开发板上处理1500万像素的图像仅耗时1.73 s。

The problem of current algorithms is lack of timeliness and reliability in bridge crack detection.In this paper,a real-time algorithm on embedded platform is proposed.Firstly,moving average method is used to segment the image coarsely.Then,candidate crack fragments are selected by region growing method using the geometric features of their contours.Finally,a crack merging model with two criteria is built to merge the crack fragments recursively and suppress interference,based on the prior condition of bridge cracks.Experimental results show that the proposed method performs better than several existing methods by 115%at least,especially on extracting hairline cracks and complex cases with uneven illumination and dirty mark.Dealing with an image with 15 megapixel on embedded platform,it costs only 1.73 s.