有效的视觉显著性方法能准确快速地帮助人们在大量视觉信息中找到感兴趣的物体。针对实际路面图像噪声成分复杂、覆盖面广的特点,提出一种基于图像显著性的路面裂缝检测算法。该算法对路面裂缝图像分块灰度校正后,根据灰度稀疏性、全局对比度计算粗尺度下的裂缝显著值,然后由裂缝局部亮度、边缘特性、连续性特点进行不断扩张的细尺度的局部邻域显著性增强,再经空间显著性加强后,采用自适应阈值分割提取裂缝。大量的实验结果表明,该算法比传统算法更能正确、有效地检测出裂缝整体区域,抗噪声能力强,漏检率和误检率很低,具有和人类视觉特性相符合的检测结果。

An effective approach for visual saliency detection can help people search for the object of interest from vast visual information rapidly and accurately. Considering the complexity of noises covering a wide area in actual road images, we present a new pavement crack detection approach based on image saliency in this paper. This approach calculates the salient value of crack images in a coarse scale based on the grayscale sparsity and global contrast after grayscale correction on images, which are divided into small blocks. Then, according to the characteristics of the cracks, such as local brightness, edge, and continuity, we calculate the local saliency in the continuously outspread local neighbor domain in a fine scale. After enhancing the saliency based on the spatial continuity, we extract cracks using adaptive image segmentation method. A large number of experimental results demonstrate this approach can detect the crack areas more correctly and effectively compared with traditional methods. It better suppress noises, has lower missing rate and misuse detection rate. Moreover, the result is consistent with human visual characteristics.