.png){kind=icon}
.png){kind=icon}
Return
Hybrid CNN-Mamba Network and Air-Ground Platform for Pavement Crack Evaluation
.png){kind=icon}
PRE
AI
L
D
Y
P
R
X
H
X
DOI：10.1109/tits.2026.3673474.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Pavement crack detection is a fundamental task for maintaining the stability and sustainability of transportation infrastructure systems. Previous convolutional neural network (CNN) and Transformer-based methods have achieved high accuracy in general crack detection. However, processing slender crack images collected by uncrewed aerial vehicles (UAVs) and inspection vehicles remains extremely challenging. To this end, this paper proposes a pavement crack evaluation framework using a hybrid CNN-Mamba network and an air-ground platform. Firstly, the framework employs an air-ground platform composed of UAVs and vehicles to collect pavement crack data and perform preprocessing. Secondly, a hybrid CNN-Mamba network with wavelet transform, named WTCMamba, is proposed to achieve crack segmentation. Specifically, the network consists of a lightweight encoder composed of multiple dual convolution (DC) modules, a Mamba decoder, and a contextual spatial feature propagation (CSFP) module. The key innovation of the network is that the Mamba decoder comprises multiple wavelet-guided Mamba (WGM) modules, which introduce wavelet transform to convert feature channels into the frequency domain and fuse features in the global space. Finally, a grid-based quantitative risk evaluation method and a correlation analysis method are employed to analyze the overall risk of crack clusters and the morphological features of individual cracks. In addition, WTCMamba is deployed and tested on edge computing devices, achieving 35.63 FPS. Extensive experiments demonstrate that WTCMamba achieves excellent crack detection performance using only 2.31 M parameters and outperforms 12 state-of-the-art methods.
Keywords:
Slender crack detection
CNN
Mamba
wavelet transform
edge deployment
Journal
IF:
8.4
Papers:
9.5K
Citations:
6.3W
Organization
Cited Papers