In this paper, we rethink our earlier work on self-attention based crack segmentation, and propose an upgraded CrackFormer network (CrackFormer-II) for pavement crack segmentation, instead of only for fine-grained crack-detection tasks. This work embeds novel Transformer encoder modules into a SegNet-like encoder-decoder structure, where the basic module is composed of novel Transformer encoder blocks with effective relative positional embedding and long range interactions to extract efficient contextual information from feature-channels. Further, fusion modules of scaling-attention are proposed to integrate the results of each respective encoder and decoder block to highlight semantic features and suppress non-semantic ones. Moreover, we update the Transformer encoder blocks enhanced by the local feed-forward layer and skip-connections, and optimize the channel configurations to compress the model parameters. Compared with the original CrackFormer, the CrackFormer-II is trained and evaluated on more general crack datasets. It achieves higher accuracy than the original CrackFormer, and the state-of-the-art (SOTA) method with $6.7 \times $ fewer FLOPs and $6.2 \times $ fewer parameters, and its practical inference speed is comparable to most classical CNN models. The experimental results show that it achieves the F-measures on Optimal Dataset Scale (ODS) of 0.912, 0.908, 0.914 and 0.869, respectively, on the four benchmarks. Codes are available at https://github.com/LouisNUST/CrackFormer-II.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    CrackFormer Network for Pavement Crack Segmentation


    Contributors:
    Liu, Huajun (author) / Yang, Jing (author) / Miao, Xiangyu (author) / Mertz, Christoph (author) / Kong, Hui (author)


    Publication date :

    2023-09-01


    Size :

    3586217 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    CrackW-Net: A Novel Pavement Crack Image Segmentation Convolutional Neural Network

    Han, Chengjia / Ma, Tao / Huyan, Ju et al. | IEEE | 2022


    The Improvement of Automated Crack Segmentation on Concrete Pavement with Graph Network

    Jiang Chen / Ye Yuan / Hong Lang et al. | DOAJ | 2022

    Free access

    PAF-Net: A Progressive and Adaptive Fusion Network for Pavement Crack Segmentation

    Yang, Lei / Huang, Hanyun / Kong, Shuyi et al. | IEEE | 2023


    MorFormer: Morphology-Aware Transformer for Generalized Pavement Crack Segmentation

    Guo, Xin / Tang, Wenzhong / Wang, Haoran et al. | IEEE | 2025


    Automatic Pavement Crack Rating for Network-Level Pavement Management System

    Tan, Jun Yew / Nguyen, Teron / Kapilan, S. et al. | TIBKAT | 2022