邻域与梯度显著特征融合的沥青路面裂缝检测方法

Asphalt Pavement Crack Detection Based on Fusion of Neighborhood and Gradient Salient Features

针对沥青路面裂缝检测中富纹理噪声影响和细小裂缝误识别严重等问题,提出一种邻域与梯度显著特征融合的沥青路面裂缝检测方法.首先采用灰度校正和形态学重建降低外界干扰和富纹理中较亮点状噪声导致亮度不均的影响,根据像素及其邻域的显著差异提取邻域显著特征,通过方向可调滤波器得到不同方向上的梯度显著特征,将两者卷积融合并优选方向生成特征融合显著图;然后对特征融合显著图阈值分割得到疑似裂缝聚集区域,结合聚集区域的不同几何特征引入聚类分析法筛选裂缝候选区域;最后提出区域端点搜索与定位法,剔除无端点聚集区域的子集,并连接不同区域端点,最终实现裂缝较完整提取.在采集的沥青路面裂缝图像数据集上的实验结果表明,该方法的准确率、召回率、F值分别为92.857%, 86.405%和89.515%,可有效地检测沥青路面图像裂缝,尤其能识别细小裂缝,为路面养护工作提供更准确的裂缝信息.

In order to solve the problem of the rich texture noise and serious misidentification of small cracks in the asphalt pavement crack detection,, an asphalt pavement crack detection method that combines neighborhood and gradient salient features is proposed. Firstly, the grayscale correction and morphological reconstruction are used to reduce the influence of external interference and uneven brightness caused by the bright spot noise in the rich texture, then the neighborhood salient features are extracted according to the salient differences between pixels and their neighborhood, and gradient salient features in different directions are obtained through the steerable filter. With the convolutional fusion of the neighborhood and gradient salient features, preferential directions are chosen to generate the salient map of feature fusion. Secondly, the suspected crack aggregation areas are obtained by the threshold segmentation of salient map. Based on the different geometric characteristics of the aggregation areas, a clustering analysis method is introduced to select the crack candidate area. Finally, the method of searching and locating a regional endpoint is proposed to eliminate the subsets without endpoint aggregation area, the endpoints of different regions are connected to achieve the complete crack extraction. The experimental results of the collected asphalt pavement crack image datasets show that the precision, recall and F-measure value are 92.857%, 86.405% and 89.515%, which can effectively detect cracks in asphalt pavement images, especially for small cracks, and provide more accurate crack information for pavement maintenance.