Abstract
Pavement defects pose serious threats to traffic safety, pavement durability, and operational efficiency. To achieve accurate and real-time identification of pavement defects, this study proposes an enhanced lightweight model, YOLO11-WLBS, which integrates four improved modules—Wavelet Transform Convolution, Lightweight Adaptive Extraction, Bidirectional Feature Pyramid Network, and Simple Attention—into the YOLO11 framework. Each module’s contribution is verified through ablation experiments. The proposed model achieves a precision of 0.947, recall of 0.895, F1-score of 0.895, mAP@0.5 of 0.944, and mAP@0.5–0.95 of 0.703, demonstrating high accuracy and efficiency. Compared with the baseline YOLO11, YOLO11-WLBS improves precision by 6.4%, recall by 15.8%, and mAP@0.5 by 12.2%, while reducing parameters by 25.5%. The model maintains excellent detection performance under extreme lighting and blurring conditions and exhibits strong generalization in cross-dataset applications. These results indicate that YOLO11-WLBS provides an efficient and robust solution for intelligent pavement defect detection and offers practical potential for real-time deployment on edge devices in pavement maintenance and infrastructure monitoring systems.
Similar content being viewed by others
Data availability
The data used to support the findings of this study are available from the corresponding author upon reasonable request.
References
Cao, W., Liu, Q. & He, Z. Review of pavement defect detection methods. IEEE Access. 8, 14531–14544. https://doi.org/10.1109/ACCESS.2020.2966881 (2020).
Zhang, L., Xu, W., Zhu, L., Yuan, X. & Zhang, C. Study on pavement defect detection based on image processing utilizing UAV. J. Phys: Conf. Ser. 042011 https://doi.org/10.1088/1742-6596/1168/4/042011 (2019).
Fan, L. et al. Pavement defect detection with deep learning: A comprehensive survey. IEEE Trans. Intell. Veh. 9, 4292–4311. https://doi.org/10.1109/TIV.2023.3326136 (2023).
Zakeri, H., Nejad, F. M. & Fahimifar, A. Image based techniques for crack detection, classification and quantification in asphalt pavement: a review. Arch. Comput. Methods Eng. 24, 935–977. https://doi.org/10.1007/s11831-016-9194-z (2017).
Koch, C., Georgieva, K., Kasireddy, V., Akinci, B. & Fieguth, P. A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure. Adv. Eng. Inform. 29, 196–210. https://doi.org/10.1016/j.aei.2015.01.008 (2015).
Cafiso, S., D’Agostino, C., Delfino, E. & Montella, A. From manual to automatic pavement distress detection and classification. 2017 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems. 433–438. (2017). https://doi.org/10.1109/MTITS.2017.8005711
Benmhahe, B. & Chentoufi, J. A. Automated pavement distress detection, classification and measurement: A review. Int. J. Adv. Comput. Sci. Appl. 12 https://doi.org/10.14569/IJACSA.2021.0120882 (2021).
Alnaqbi, A., Al-Khateeb, G. G. & Zeiada, W. Optimized prediction of longitudinal cracking in concrete pavements using hybrid GA-GBM models. J. Building Pathol. Rehabilitation. 10, 156. https://doi.org/10.1007/s41024-025-00667-9 (2025).
Alnaqbi, A., Al-Khateeb, G. G., Zeiada, W. & Abuzwidah, M. Random forest-based frame work for multi-distress prediction in CRCP: a feature importance approach. Discover Civil Eng. 2, 140. https://doi.org/10.1007/s44290-025-00302-z (2025).
Alnaqbi, A., Al-Khateeb, G. G. & Zeiada, W. Genetic Algorithm-Enhanced gradient boosting for transverse cracking in CRCP. Jordan J. Civil Eng. 19 https://doi.org/10.14525/JJCE.v19i2.11 (2025).
Ma, J., Jiang, X., Fan, A., Jiang, J. & Yan, J. Image matching from handcrafted to deep features: A survey. Int. J. Comput. Vision. 129, 23–79. https://doi.org/10.1007/s11263-020-01359-2 (2021).
Dargan, S., Kumar, M., Ayyagari, M. R. & Kumar, G. A survey of deep learning and its applications: a new paradigm to machine learning. Arch. Comput. Methods Eng. 27, 1071–1092. https://doi.org/10.1007/s11831-019-09344-w (2020).
Arya, D. et al. Deep learning-based road damage detection and classification for multiple countries. Autom. Constr. 132, 103935. https://doi.org/10.1016/j.autcon.2021.103935 (2021).
Hsieh, C. C., Jia, H. W., Huang, W. H. & Hsih, M. H. Deep Learning-Based Road Pavement Inspection by Integrating Visual Information and IMU. Information, 15, 239. (2024). https://doi.org/10.3390/info15040239
Alzubaidi, L. et al. Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. J. Big Data. 8, 53. https://doi.org/10.1186/s40537-021-00444-8 (2021).
Tong, Z., Gao, J. & Zhang, H. Innovative method for recognizing subgrade defects based on a convolutional neural network. Constr. Build. Mater. 169, 69–82. https://doi.org/10.1016/j.conbuildmat.2018.02.081 (2018).
Redmon, J., Divvala, S., Girshick, R. & Farhadi, A. You only look once: Unified, real-time object detection. Proceedings of the IEEE conference on computer vision and pattern recognition. 779–788 (2016).
Girshick, R. & Fast, R-C-N-N. Proceedings of the IEEE international conference on computer vision. 1440–1448 (2015).
Bharati, P. & Pramanik, A. Deep learning techniques—R-CNN to mask R-CNN: a survey. Comput. Intell. Pattern Recognition: Proc. CIPR 2019. 657–668 https://doi.org/10.1007/978-981-13-9042-5_56 (2019).
Liu, W. et al. Ssd: Single shot multibox detector. Computer Vision–ECCV. : 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14. 21–37. (2016). https://doi.org/10.1007/978-3-319-46448-0_2 (2016).
Terven, J., Córdova-Esparza, D. M. & Romero-González J.-A. A comprehensive review of Yolo architectures in computer vision: from Yolov1 to Yolov8 and Yolo-nas. Mach. Learn. Knowl. Extr. 5, 1680–1716. https://doi.org/10.3390/make5040083 (2023).
Du, F. J. & Jiao, S. J. Improvement of lightweight convolutional neural network model based on YOLO algorithm and its research in pavement defect detection. Sensors 22, 3537. https://doi.org/10.3390/s22093537 (2022).
Majidifard, H., Jin, P., Adu-Gyamfi, Y. & Buttlar, W. G. Pavement image datasets: A new benchmark dataset to classify and densify pavement distresses. Transp. Res. Rec. 2674, 328–339. https://doi.org/10.1177/0361198120907283 (2020).
Ma, L. & Chen, M. Road damage detection based on improved YOLO algorithm. Sci. Rep. 15, 28506. https://doi.org/10.1038/s41598-025-14461-7 (2025).
Zhang, S., Bei, Z., Ling, T., Chen, Q. & Zhang, L. Research on high-precision recognition model for multi-scene asphalt pavement distresses based on deep learning. Sci. Rep. 14, 25416. https://doi.org/10.1038/s41598-024-77173-4 (2024).
Li, Z. et al. Lightweight pavement crack detection model for edge computing devices. Sci. Rep. 15, 38179. https://doi.org/10.1038/s41598-025-22092-1 (2025).
Dong, S. et al. Advanced lightweight deep learning vision framework for efficient pavement damage identification. Sci. Rep. 15, 12966. https://doi.org/10.1038/s41598-025-97132-x (2025).
Mulyanto, A., Sari, R. F., Muis, A. & Harwahyu, R. Vision-Based automated pavement distress inspection: A review. IEEE Access. https://doi.org/10.1109/TITS.2021.3113802 (2025).
Wang, C. et al. -HR: implementing lightweight and efficient pavement distress detection by enhancement of the Spatial information extractions of high-resolution features. Eng. Res. Express. 7, 045226. https://doi.org/10.1088/2631-8695/ae0f01 (2025).
Sapkota, R. et al. YOLO advances to its genesis: a decadal and comprehensive review of the you only look once (YOLO) series. Artif. Intell. Rev. 58, 274. https://doi.org/10.1007/s10462-025-11253-3 (2025).
Ali, M. L. & Zhang, Z. The YOLO framework: A comprehensive review of evolution, applications, and benchmarks in object detection. Computers 13, 336. https://doi.org/10.3390/computers13120336 (2024).
Jiang, P., Ergu, D., Liu, F., Cai, Y. & Ma, B. A review of Yolo algorithm developments. Procedia Comput. Sci. 199, 1066–1073. https://doi.org/10.1016/j.procs.2022.01.135 (2022).
Aicha, M. Techniques and applications of image and signal processing: A theoretical approach. 8th Int. Conf. Image Signal. Process. Their Appl. (ISPA). 1-8 https://doi.org/10.1007/s10462-025-11253-3 (2024). (2024).
Xiang, S. & Liang, Q. Remote sensing image compression based on high-frequency and low-frequency components. IEEE Trans. Geosci. Remote Sens. 62, 1–15. https://doi.org/10.1109/TGRS.2023.3349306 (2024).
Zhang, X., Wang, X. & Yang, Z. A. Lightweight pavement defect detection algorithm integrating perception enhancement and feature optimization. Sensors 25, 4443. https://doi.org/10.3390/s25144443 (2025).
Daubechies, I. Orthonormal bases of compactly supported wavelets. Commun. Pure Appl. Math. 41, 909–996. https://doi.org/10.1002/cpa.3160410705 (1988).
Heil, C. E. & Walnut, D. F. Continuous and discrete wavelet transforms. SIAM Rev. 31, 628–666. https://doi.org/10.1137/1031129 (1989).
Lin, T. Y. et al. Feature pyramid networks for object detection. Proceedings of the IEEE conference on computer vision and pattern recognition. 2117–2125 (2017).
Chen, P. Y., Chang, M. C., Hsieh, J. W. & Chen, Y. S. Parallel residual bi-fusion feature pyramid network for accurate single-shot object detection. IEEE Trans. Image Process. 30, 9099–9111. https://doi.org/10.1109/TIP.2021.3118953 (2021).
Zhang, H. et al. A recursive attention-enhanced bidirectional feature pyramid network for small object detection. Multimedia Tools Appl. 82, 13999–14018. https://doi.org/10.1007/s11042-022-13951-4 (2023).
Yang, L., Zhang, R. Y., Li, L., Xie, X. & Simam A simple, parameter-free attention module for convolutional neural networks. International conference on machine learning. 11863–11874 (2021).
Muraina, I. Ideal dataset splitting ratios in machine learning algorithms: general concerns for data scientists and data analysts. 7th international Mardin Artuklu scientific research conference. 496–504 (2022).
Padilla, R., Netto, S. L. & Da Silva, E. A. A survey on performance metrics for object-detection algorithms. 2020 international conference on systems, signals and image processing (IWSSIP). 237–242. (2020). https://doi.org/10.1109/IWSSIP48289.2020.9145130
Arman, M. S. et al. Detection and classification of road damage using R-CNN and faster R-CNN: a deep learning approach. Cyber Security and Computer Science: Second EAI International Conference, ICONCS 2020, Dhaka, Bangladesh, February 15–16, 2020, Proceedings 2. 730–741. (2020). https://doi.org/10.1007/978-3-030-52856-0_58
Jocher, G. et al. ultralytics/yolov5: v3. 0. Zenodo (2020).
Sohan, M., Sai Ram, T. & Rami Reddy, C. V. A review on yolov8 and its advancements. International Conference on Data Intelligence and Cognitive Informatics. 529–545. (2024). https://doi.org/10.1007/978-981-99-7962-2_39
Wang, C. Y., Yeh, I. H. & Mark Liao, H. Y. Yolov9: Learning what you want to learn using programmable gradient information. European conference on computer vision. 1–21. (2024). https://doi.org/10.1007/978-3-031-72751-1_1
Wang, A. et al. Yolov10: Real-time end-to-end object detection. Adv. Neural. Inf. Process. Syst. 37, 107984–108011. https://doi.org/10.52202/079017-3429 (2024).
Khanam, R. & Hussain, M. Yolov11: an overview of the key architectural enhancements. ArXiv Preprint arXiv:2410 17725. https://doi.org/10.48550/arXiv.2410.17725 (2024).
Insany, G. P., Indriyani, R., Ma’wa, N. J. & Safitri, S. Performance analysis of YOLOv11: Nano, Small, and medium models for herbal leaf classification. Eng. Proc. 107, 102. https://doi.org/10.3390/engproc2025107102 (2025).
He, L., Zhou, Y., Liu, L., Cao, W. & Ma, J. -h. Research on object detection and recognition in remote sensing images based on YOLOv11. Sci. Rep. 15, 14032. https://doi.org/10.1038/s41598-025-96314-x (2025).
Jegham, N., Koh, C. Y., Abdelatti, M. & Hendawi, A. Yolo evolution: A comprehensive benchmark and architectural review of yolov12, yolo11, and their previous versions. arXiv preprint arXiv:2411.00201. (2024). https://doi.org/10.48550/arXiv.2411.00201
Zeiler, M. D. & Fergus, R. Visualizing and understanding convolutional networks. European conference on computer vision. 818–833. (2014). https://doi.org/10.1007/978-3-319-10590-1_53
Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. Proceedings of the IEEE international conference on computer vision. 618–626 (2017).
Rahman, S., Rahman, M. M., Abdullah-Al-Wadud, M., Al-Quaderi, G. D. & Shoyaib, M. An adaptive gamma correction for image enhancement. EURASIP J. Image Video Process. 2016, 35. https://doi.org/10.1186/s13640-016-0138-1 (2016).
Dumitrescu, D. & Boiangiu, C. A. A study of image upsampling and downsampling filters. Computers 8, 30. https://doi.org/10.3390/computers8020030 (2019).
Packer, C. et al. Assessing generalization in deep reinforcement learning. ArXiv Preprint arXiv:1810 12282. https://doi.org/10.48550/arXiv.1810.12282 (2018).
Zhang, Y. et al. Road damage detection using UAV images based on multi-level attention mechanism. Autom. Constr. 144, 104613. https://doi.org/10.1016/j.autcon.2022.104613 (2022).
David, R. et al. Tensorflow lite micro: Embedded machine learning for tinyml systems. Proc. Mach. Learn. Res. 3, 800–811 (2021).
Kim, S., Park, G. & Yi, Y. Performance evaluation of INT8 quantized inference on mobile GPUs. IEEE Access. 9, 164245–164255. https://doi.org/10.1109/ACCESS.2021.3133100 (2021).
Sietsma & Dow. international conference on neural networks. 325–333 vol. 321. Neural net pruning-why and how. IEEE (1988). https://doi.org/10.1109/ICNN.1988.23864 (1988).
Acknowledgements
This work was supported by the National Natural Science Foundation of China (Grant No. 42474126, 42104094, and 42007261), and the Special Fund Project for Scientific and Technological Innovation of Fujian Agriculture and Forestry University (Grant No. KFB25044 and KFB24054). We appreciate four anonymous reviewers for their constructive suggestions and comments.
Author information
Authors and Affiliations
Contributions
J.L. conceived the study, designed the experiments, and wrote the manuscript. P.W. implemented the model and conducted the experiments. Y.R. and Y.S. supervised the research, contributed to the conceptualization and methodology, and reviewed and revised the manuscript. All authors read and approved the final version of the manuscript.
Corresponding authors
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
About this article
Cite this article
Lin, J., Wang, P., Ruan, Y. et al. YOLO11-WLBS: an efficient model for pavement defect detection. Sci Rep (2026). https://doi.org/10.1038/s41598-026-35743-8
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41598-026-35743-8
