为实现复杂背景下裂纹目标的有效检测,提出一种融合小波边缘检测与多尺度结构化森林的裂纹分割方法,以提高裂纹检测稳健性。该方法利用多幅裂纹图像和人工标注结果提取裂纹图像特征通道并离散化映射标准结果;利用三角滤波器和降采样方法获取常规和相关性候选特征;并将该特征与离散化后的标签进行结构化森林分类器的训练和验证,进而获得多个尺度的裂纹分割。在776幅结构体裂纹图像和600幅钢梁裂纹图像数据集上进行实验,结果表明,相比于单一多尺度结构化森林方法和其他几种分割方法,本文方法能够在较短的时间内获得最高的分割精度。

In order to effectively detect crack, a crack segmentation method using multi-scale structured forests and wavelet transform is proposed to improve robust performance of crack detection. The multi-channel feature extraction of crack image, and discrete mapping of the corresponding ground truth is carried out respectively with assistance of multiple crack image and ground truth. Triangle filter and down-sample are adopted to process regularity candidate features and correlation candidate features, which are used to train and validate structured forest classifier. And, structured forest classifier is used to crack segmentation of test images in multi-scale. According to experiment results in 776 structural crack image and 600 steel beam image datasets, the proposed method can obtain highest segmentation accuracy in a short time than single multi-scale structured forest method and other segmentation methods.