Crack Unet: Crack Recognition Algorithm Based on Three-Dimensional Ground Penetrating Radar Images

Abstract

Three-dimensional (3D) ground-penetrating radar is an effective method for detecting internal crack damage in pavement structures. Inefficient manual interpretation of radar images and high personnel requirements have substantially restrained the generalization of 3D ground-penetrating radar. An improved Crack Unet model based on the Unet semantic segmentation model is proposed herein for 3D ground-penetrating radar crack image processing. The experiment showed that the MPA, MioU, and accuracy of the model were improved, and it displayed better capacity in the radar image crack segmentation task than current mainstream algorithms do, such as deepLabv3, PSPNet, and Unet. In the test dataset without cracks, Crack Unet is on the same level as deepLabv3 and PSPNet, which can meet engineering requirements and display a significant improvement compared with Unet. According to the ablation experiment, the MPA and MioU of Unet configured with PMDA, MC-FS, and RS modules were larger than those of Unet configured with one or two modules. The PMDA module adopted by the Crack Unet model showed a higher MPA and MioU than the SE module and the CBAM module did, respectively. The results show that the Crack Unet model has a better segmentation ability than the current mainstream algorithms do in the task of the crack segmentation of radar images, and the performance of crack segmentation is significantly improved compared with the Unet model. The Crack Unet model has excellent engineering application value in the task of the crack segmentation of radar images.

The automated segmentation and enhancement of cracks on airport pavements using three-dimensional imaging techniques

“…Within this research, the SMFB approach employs secondary-derivative Gaussians as its fundamental filters. The 2D Gaussian with a variance of is mathematically represented by Equation (1), while its second derivatives are delineated by Equation (4,5). The formulation of the filter ( , ), where the orientation lies within the range of [− /2, /2], is elucidated in Equation (6,7).…”

“…In recent years, automated methods based on computer vision and image processing techniques have made significant progress in the field of pavement crack detection. In particular, the utilisation of 3D images has provided new possibilities for the accurate analysis of pavement cracks [1] [5]. The aim of this paper is to explore automatic segmentation and enhancement methods for airport pavement cracks with the help of 3D image technology in order to improve the accuracy and efficiency of crack detection [6].…”

Application of clustering algorithms to void recognition by 3D ground penetrating radar

“…Road materials generally have a relative dielectric constant between 3 and 10. Electromagnetic waves emitted by 3D ground penetrating radar are reflected at the void-road material interface (FIROOZABADI et al, 2007;Leng and Al-Qadi, 2014;Kumlu, 2021;Tang et al, 2022b). After the ground-penetrating radar receives the reflected electromagnetic wave signal, according to the signal propagation time and amplitude, it is recorded and stored as 3D ground penetrating radar data, and Examiner software is used to filter the data, the parameters are set as shown in the Figure 2, and the radar grey-scale image is drawn, as shown in the Figure 3.…”