融入视觉注意机制的路面裂缝检测与识别

Pavement Crack Detection and Recognition Fusing with Visual Attention Mechanism

针对实际路面裂缝检测中存在复杂噪声干扰的问题,根据裂缝和背景像素的视觉差异,设计一种融入视觉注意机制的路面裂缝自动检测与识别算法。通过灰度校正和各向异性扩散滤波的预处理方法滤除部分噪声,利用全局和局部灰度对比度信息计算裂缝显著值,融合生成综合显著图粗定位裂缝区域。在此基础上,依据裂缝区域的形状特征使用形状分析法进行去噪和目标提取,实现裂缝的精确定位。实验结果表明,该算法能快速有效地检测出裂缝区域,与基于阈值分割、边缘检测或小波变换的裂缝检测算法相比,具有较高的检测精度。

Considering the problem that complex noise interference exists in the practical pavement crack detection,by using the visual difference between cracks and background pixels,an algorithm for automatic detection and recognition of pavement crack with visual attention mechanism is proposed.Firstly,pre-processing methods of grayscale correction and anisotropic diffusion filtering are adopted to remove noises.Then,the salient values are calculated separately using global and local grayscale contrast to generate a comprehensive saliency map,where cracks are roughly positioned on.Finally,according to the shape characteristics of cracks,the shape analysis method is used to remove noises and extract targets to realize the accurate location of cracks.Experimental results show that the proposed algorithm can detect cracks quickly and efficiently.It has higher detection accuracy compared with traditional crack detection algorithms based on threshold segmentation,edge detection or wavelet transform.