AW-HRNet: A Lightweight High-Resolution Crack Segmentation Network Integrating Spatial Robustness and Frequency-Domain Enhancement
Abstract
The study presents AW-HRNet, a lightweight high-resolution crack segmentation network that couples Adaptive residual enhancement (AREM) in the spatial domain with Wavelet-based decomposition–reconstruction (WDRM) in the frequency domain. AREM introduces a learnable channel-wise scaling after standard 3 × 3 convolution and merges it through a residual path to stabilize crack-sensitive responses while suppressing noise. WDRM performs DWT to decouple LL/LH/HL/HH sub-bands, conducts lightweight cross-band fusion, and applies IDWT to restore detail-enhanced features, unifying global topology and boundary sharpness without deformable offsets. Integrated into a high-resolution backbone with auxiliary deep supervision, AW-HRNet attains 79.07% mIoU on CrackSeg9k with only 1.24M parameters and 0.73 GFLOPs, offering an excellent accuracy–efficiency trade-off and strong robustness for real-world deployment.
References
Yuan Q, Shi Y, Li M, 2024, A Review of Computer Vision-Based Crack Detection Methods in Civil Infrastructure: Progress and Challenges. Remote Sensing, 16(16): 2910.
Gavilán M, Balcones D, Marcos O, et al., 2011, Adaptive Road Crack Detection System by Pavement Classification. Sensors, 11(10): 9628–9657.
Kim B, Cho S, 2018, Automated Vision-Based Detection of Cracks on Concrete Surfaces Using a Deep Learning Technique. Sensors, 18(10): 3452.
Huang S, Chen H, Yan L, et al., 2025, A Review of the Progress in Machine Vision-Based Crack Detection and Identification Technology for Asphalt Pavements. Digital Transportation and Safety, 4(1): 65–79.
Zawad M, Zawad M, Rahman M, et al., 2021, A Comparative Review of Image Processing Based Crack Detection Techniques on Civil Engineering Structures. Journal of Soft Computing in Civil Engineering, 5(3): 58–74.
Shi Y, Cui L, Qi Z, et al., 2016, Automatic Road Crack Detection Using Random Structured Forests. IEEE Transactions on Intelligent Transportation Systems, 17(12): 3434–3445.
Koch C, Brilakis I, 2011, Pothole Detection in Asphalt Pavement Images. Advanced Engineering Informatics, 25(3): 507–515.
Amhaz R, Chambon S, Idier J, et al., 2016, Automatic Crack Detection on Two-Dimensional Pavement Images: An Algorithm Based on Minimal Path Selection. IEEE Transactions on Intelligent Transportation Systems, 17(10): 2718–2729.
Zhang L, Yang F, Zhang Y, et al., 2016, Road Crack Detection Using Deep Convolutional Neural Network. Proceedings of the IEEE International Conference on Image Processing (ICIP), 3708–3712.
Ronneberger O, Fischer P, Brox T, 2015, U-Net: Convolutional Networks for Biomedical Image Segmentation. International Conference on Medical Image Computing and Computer-Assisted Intervention. Cham: Springer International Publishing, 234–241.
Lin T, Dollár P, Girshick R, et al., 2017, Feature Pyramid Networks for Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2117–2125.
Hu J, Shen L, Sun G, 2018, Squeeze-and-Excitation Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 7132–7141.
Dai J, Qi H, Xiong Y, et al., 2017, Deformable Convolutional Networks. Proceedings of the IEEE International Conference on Computer Vision, 764–773.
Zhu X, Hu H, Lin S, et al., 2019, Deformable ConvNets v2: More Deformable, Better Results. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 9308–9316.
Iandola F, Han S, Moskewicz M, et al., 2016, SqueezeNet: AlexNet-Level Accuracy with 50× Fewer Parameters and <0.5 MB Model Size. arXiv Preprint arXiv:1602.07360.
Howard A, Zhu M, Chen B, et al., 2017, MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv Preprint arXiv:1704.04861.
Zhang X, Zhou X, Lin M, et al., 2018, ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 6848–6856.
Zawad M, Zawad M, Rahman M, et al., 2021, A Comparative Review of Image Processing Based Crack Detection Techniques on Civil Engineering Structures. Journal of Soft Computing in Civil Engineering, 5(3): 58–74.
Yuan F, Lin Z, Tian Z, et al., 2025, Bio-Inspired Hybrid Path Planning for Efficient and Smooth Robotic Navigation: F. Yuan et al. International Journal of Intelligent Robotics and Applications, 2025: 1–31.
Liang B, Yuan F, Deng J, et al., 2025, CS-PBFT: A Comprehensive Scoring-Based Practical Byzantine Fault Tolerance Consensus Algorithm. The Journal of Supercomputing, 81(7): 859.
Zhang K, Yuan F, Jiang Y, et al., 2025, A Particle Swarm Optimization-Guided Ivy Algorithm for Global Optimization Problems. Biomimetics, 10(5): 342.
Yuan F, Huang X, Jiang H, et al., 2025, An xLSTM–XGBoost Ensemble Model for Forecasting Non-Stationary and Highly Volatile Gasoline Price. Computers, 14(7): 256.
Kulkarni S, Singh S, Balakrishnan D, et al., 2022, CrackSeg9k: A Collection and Benchmark for Crack Segmentation Datasets and Frameworks. European Conference on Computer Vision. Cham: Springer Nature Switzerland, 179–195.
Zhao H, Shi J, Qi X, et al., 2017, Pyramid Scene Parsing Network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2881–2890.
Yu C, Gao C, Wang J, et al., 2021, BiSeNet v2: Bilateral Network with Guided Aggregation for Real-Time Semantic Segmentation. International Journal of Computer Vision, 129(11): 3051–3068.
Hong Y, Pan H, Sun W, et al., 2021, Deep Dual-Resolution Networks for Real-Time and Accurate Semantic Segmentation of Road Scenes. arXiv Preprint arXiv:2101.06085.
Li Y, Ma R, Liu H, et al., 2023, Real-Time High-Resolution Neural Network with Semantic Guidance for Crack Segmentation. Automation in Construction, 156: 105112.
