针对单张沥青路面图像中裂缝的高效语义级修复

原始无损路面图像对分析路面损伤演化细节及制定下一步养护方案具有重要意义,而实地采集中无法获取路面裂缝图像对应的初始状态.为了获取其对应的无损路面图像,本文提出了一种基于深度图像先验的无监督沥青路面裂缝图像修复算法,可实现对单张路面图像中裂缝的高效语义级修复.首先采用鲁棒主成分分析算法去除路面裂缝图像表面的竖状条纹噪声.随后,采用最大类间方差法及形态学处理得到裂缝区域的二进制掩码图像.最后,运用提出的深度图像先验修复算法对裂缝区域进行修复得到最终的无损路面图像.在自采集路面裂缝图像数据集上对所提方法进行了评估.实验结果表明,所提方法能够有效实现路面裂缝图像语义级修复,峰值信噪比和结构相似性较传统的方法有了明显提升,平均达到了43.382 3 dB和0.983 4,且兼具高速度.

Efficient Semantic-level Inpainting for Cracks in Single Asphalt Pavement Image

The original damage-free pavement image is of great significance for analyzing the evolution details of pavement damages and formulating the next maintenance plan. However, the initial state corresponding to a pavement crack image cannot be obtained in field acquisition. To obtain the corresponding damage-free pavement image, this study proposes a deep image prior-based unsupervised crack image inpainting algorithm for asphalt pavements that enables efficient semantic-level inpainting of cracks in a single pavement image. Specifically, a robust principal component analysis algorithm is used to remove the vertical stripe noise on the surface of the pavement crack image. Then, the maximum between-class variance method and morphological processing are employed to obtain a binary mask image of the crack area. Finally, the crack area is inpainted with the proposed deep image prior-based inpainting algorithm to obtain the final damage-free pavement image. The proposed method is evaluated on a dataset of self-collected pavement crack images. The experimental results show that the proposed method can effectively achieve semantic-level inpainting of pavement crack images as it significantly improves the peak signal-to-noise ratio and structural similarity to an average of 43.3823 dB and 0.9834, respectively, compared with those of the traditional methods and it also achieves a high speed.