基于图像转换的道路图像裂缝检测  

Road Image Crack Detection Based on Image Transformation

作　　者：阎志文 YAN Zhi-Wen(School of Information Engineering,Chang’an University,Xi’an 710064,China)

机构地区：[1]长安大学信息工程学院,西安710064

出　　处：《计算机系统应用》2023年第4期241-247,共7页Computer Systems & Applications

摘　　要：深度学习是目前路面图像裂缝检测的主流方法,但是需要大量人工标注的真值图进行训练,而现实中获取人工标注的真值图既费时又费力,本文提出一种基于改进的生成对抗网络的路面图像裂缝检测方法,将路面图像裂缝检测问题视为一类基于图像跨域转换的异常检测问题,采用定点生成对抗网络将裂缝图像无监督自动转换为与之一一对应的无裂缝图像,进而将原图像与生成图像进行差分,差分图中的显著目标对应裂缝检测结果.在公开数据集CrackIT上的测试结果表明,本文方法在不依赖于人工标注的真值图条件下能够实现裂缝的精准检测,本文方法在准确率、召回率、F1分数上取得了与有监督深度学习方法相当的性能.Deep learning is currently a mainstream method for crack detection in pavement images,but it requires a large number of manually-annotated ground-truth images for training.However,in reality,it is time-consuming and laborious to obtain manually-annotated ground-truth images.This study proposes a method for crack detection in pavement images based on an improved generative adversarial network.The study regards crack detection in pavement images as a kind of anomaly detection problem based on image cross-domain transformation and uses a fixed-point generative adversarial network to automatically convert the crack image into a one-to-one corresponding crack-free image with supervision.Then,the study differentiates the original image and the generated image,and the salient objects in the difference image correspond to the crack detection results.The test results on the public dataset CrackIT show that the method in this study can achieve accurate crack detection without relying on the manually-annotated ground-truth images.In addition,the method achieves comparable performance to supervised deep learning methods in terms of precision,recall,and F1-score.

关 键 词：道路裂缝检测 定点生成对抗网络 图像转换 无监督 

分 类 号：U418.66[交通运输工程—道路与铁道工程] TP391.41[自动化与计算机技术—计算机应用技术]