Seo Hyungjoon, Shi Yunfan, Fu Lang
Department of Civil and Environmental Engineering, University of Liverpool, Liverpool L69 7WW, UK.
Department of Computer Science, University of Liverpool, Liverpool L69 7WW, UK.
Sensors (Basel). 2024 Jan 11;24(2):464. doi: 10.3390/s24020464.
It is important to maintain the safety of road driving by automatically performing a series of processes to automatically measure and repair damage to the road pavement. However, road pavements include not only damages such as longitudinal cracks, transverse cracks, alligator cracks, and potholes, but also various elements such as manholes, road marks, oil marks, shadows, and joints. Therefore, in order to separate categories that exist in various road pavements, in this paper, 13,500 digital, IR, and MSX images were collected and nine categories were automatically classified by DarkNet. The DarkNet classification accuracies of digital images, IR images, and MSX images are 97.4%, 80.1%, and 91.1%, respectively. The MSX image is a enhanced image of the IR image and showed an average of 6% lower accuracy than the digital image but an average of 11% higher accuracy than the IR image. Therefore, MSX images can play a complementary role if DarkNet classification is performed together with digital images. In this paper, a method for detecting the directionality of each crack through a two-dimensional wavelet transform is presented, and this result can contribute to future research on detecting cracks in pavements.
通过自动执行一系列过程来自动测量和修复路面损伤，对于维持道路驾驶安全很重要。然而，路面不仅包括纵向裂缝、横向裂缝、龟裂和坑洼等损伤，还包括诸如检修孔、道路标记、油渍、阴影和接缝等各种元素。因此，为了区分各种路面中存在的类别，本文收集了13500张数字图像、红外图像和MSX图像，并通过DarkNet自动分类为九类。数字图像、红外图像和MSX图像的DarkNet分类准确率分别为97.4%、80.1%和91.1%。MSX图像是红外图像的增强图像，其准确率平均比数字图像低6%，但比红外图像平均高11%。因此，如果与数字图像一起进行DarkNet分类，MSX图像可以起到补充作用。本文提出了一种通过二维小波变换检测每条裂缝方向性的方法，该结果可为未来路面裂缝检测研究做出贡献。
