基于超分辨率生成对抗网络的混凝土裂缝检测算法  

Concrete crack detection algorithm based on super-resolution generative adversarial network

作　　者：李响[1] LI Xiang(Beijing Urban Construction Survey and Design Institute Co.,Ltd.,Beijing 100101,China)

机构地区：[1]北京城建勘测设计研究院有限责任公司,北京100101

出　　处：《测绘通报》2025年第3期111-116,共6页Bulletin of Surveying and Mapping

摘　　要：随着服役年限的增长,隧道不可避免会发生老化,作为城市居民出行的重要基础设施,保障其安全是至关重要的。目前多通过相机拍摄的图像识别隧道表面的裂缝病害,然而裂缝在图像中的像素占比小,其检测过程耗时费力,急需一种能够在大视场范围内精准检测裂缝的方法。因此,本文首先提出了一种基于超分辨率生成对抗网络的学习结构,适用于任何分割网络,然后提出了一种有效构建训练数据的方法,应用于所提出的学习结构,最后对本文方法在1606张质量随机退化的裂缝图像上进行了性能评估,结果表明,本文所提出的学习结构下,裂缝检测IoU及F1分数分别达63.686%和77.811%,方差分别为0.9008和0.5015,有效提高了裂缝的检测性能,且对输入数据具有较高的稳健性。With the growth of service life,tunnels will inevitably undergo aging,and as an important infrastructure for the travelling of urban residents,tunnel safety inspection is crucial.At present,the crack disease on the tunnel surface is mostly detected using the images taken by cameras.However,the cracks have a small pixel percentage in the image,and its detection process is time-consuming and labor-intensive.Hence,there is an urgent need for a method that can accurately detect the cracks in a large field-of-view range.This paper proposes a learning structure based on super-resolution generative adversarial networks,which is applicable to any segmentation network,and proposes a method for efficiently constructing training data to be applied to the proposed learning structure.The performance of the proposed method is evaluated on 1606 crack images with randomly degraded quality.The results show that the crack detection IoU and F1 scores under the proposed learning structure are 63.686% and 77.811%,respectively,and the variances are 0.9008 and 0.5015,which effectively improves the performance of crack detection and has high robustness to the input data.

关 键 词：混凝土隧道 裂缝检测 超分辨率生成对抗网络 分割算法 

分 类 号：P237[天文地球—摄影测量与遥感]