{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,29]],"date-time":"2026-06-29T04:45:15Z","timestamp":1782708315747,"version":"3.54.5"},"reference-count":30,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2026,6,21]],"date-time":"2026-06-21T00:00:00Z","timestamp":1782000000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Drainage pipe defect detection is essential for maintaining the normal operation of urban infrastructure. In recent years, deep learning-based object detection methods have provided an effective technical solution for drainage pipe defect recognition. Among them, YOLO-series models have demonstrated strong potential in visual detection tasks due to their end-to-end architecture and high inference efficiency. However, directly applying baseline YOLO models may still face challenges such as limited detection accuracy, relatively high model complexity, and insufficient adaptability for lightweight deployment scenarios. To address these issues, this paper proposes a lightweight drainage pipe defect detection method based on an improved YOLO11 network. Rather than treating detection enhancement and model compression as two separate procedures, the proposed method integrates feature enhancement, adaptive pruning, and distillation-based recovery into a unified lightweight detection framework. Specifically, an improved SimAM attention mechanism is introduced into the backbone and integrated with the C3k2 module to construct the C3K2_SWS module, aiming to enhance the representation capability of critical defect features. In the neck network, a focused diffusion pyramid network with a dimension-aware selective fusion structure, termed FDPN-DASI, is designed to strengthen multi-scale feature interactions. In addition, an adaptive-threshold focal loss (ATFL) is introduced to improve the learning capability for hard samples. For efficient deployment, the LAMP pruning algorithm is further improved, and an entropy-guided entropy-adaptive magnitude-based pruning method (EA-LAMP) is proposed to enable adaptive allocation of pruning ratios across different network layers. Moreover, BCKD knowledge distillation is applied after pruning to mitigate the accuracy degradation caused by model compression. Experimental results indicate that the proposed lightweight YOLO11-SFA+EA+BCKD framework achieves a precision of 92.4%, a recall of 88.5%, and an mAP50 of 93.3%, while maintaining a compact model size of 1.6 M parameters and 4.5 G FLOPs. Compared with the baseline model, the proposed method improves precision, recall, and mAP50 by 5.9%, 5.0%, and 4.7%, respectively, while reducing the number of parameters, FLOPs, and model size by 1.0 M, 1.8 G, and 2.1 M, respectively. These results suggest that the proposed framework can improve detection performance while reducing model complexity under the current experimental setting, indicating its potential for lightweight drainage pipe defect detection tasks.<\/jats:p>","DOI":"10.3390\/info17060613","type":"journal-article","created":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T05:37:17Z","timestamp":1782279437000},"page":"613","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Lightweight Drainage Pipe Defect Detection Method Based on an Improved YOLO11 Network"],"prefix":"10.3390","volume":"17","author":[{"given":"Rui","family":"Xue","sequence":"first","affiliation":[{"name":"College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongtao","family":"Fu","sequence":"additional","affiliation":[{"name":"College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hui","family":"Zhao","sequence":"additional","affiliation":[{"name":"Harbin Space Star Data System Technology Co., Ltd., Harbin 150028, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chongquan","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,6,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Haurum, J.B., Madadi, M., Escalera Guerrero, S., and Moeslund, T.B. (2022, January 4\u20138). Multi-task classification of sewer pipe defects and properties using a cross-task graph neural network decoder. Proceedings of the IEEE Winter Conference on Applications of Computer Vision, Waikoloa, HI, USA.","DOI":"10.1109\/WACV51458.2022.00151"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"139923","DOI":"10.1016\/j.jclepro.2023.139923","article-title":"A state-of-the-art review for the prediction of overflow in urban sewer systems","volume":"434","author":"Ma","year":"2024","journal-title":"J. Clean. Prod."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Shen, D., Liu, X., Shang, Y., and Tang, X. (2023). Deep learning-based automatic defect detection method for sewer pipelines. Sustainability, 15.","DOI":"10.3390\/su15129164"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Ha, B., Schalter, B., White, L., and K\u00f6hler, J. (2024). Automatic defect detection in sewer network using deep learning based object detector. arXiv.","DOI":"10.5220\/0011986300003497"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 23\u201328). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.autcon.2018.08.006","article-title":"Automated detection of sewer pipe defects in closed-circuit television images using deep learning techniques","volume":"95","author":"Cheng","year":"2018","journal-title":"Autom. Constr."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201330). You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"103912","DOI":"10.1016\/j.autcon.2021.103912","article-title":"Automatic detection of sewer defects based on improved you only look once algorithm","volume":"131","author":"Tan","year":"2021","journal-title":"Autom. Constr."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Liu, R., Shao, Z., Sun, Q., and Yu, Z. (2024). Defect detection and 3D reconstruction of complex urban underground pipeline scenes for sewer robots. Sensors, 24.","DOI":"10.3390\/s24237557"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"100191","DOI":"10.1016\/j.dibe.2023.100191","article-title":"A transfer learning-based YOLO network for sewer defect detection in comparison to classic object detection methods","volume":"15","author":"Situ","year":"2023","journal-title":"Dev. Built Environ."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Dong, J., and Liao, M.C. (2024, January 27\u201329). Defect Detection of Urban Drainage Pipeline Based on Improved YOLOV8. Proceedings of the IEEE 7th International Conference on Information Systems and Computer Aided Education, Dalian, China.","DOI":"10.1109\/ICISCAE62304.2024.10761785"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"145857","DOI":"10.1109\/ACCESS.2024.3462738","article-title":"Advanced YOLO-DeepSort-Based System for Drainage Pipeline Defects Intelligent Detection","volume":"12","author":"Wang","year":"2024","journal-title":"IEEE Access"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Zhang, K., Qin, L., and Zhu, L. (2025). PDS-YOLO: A Real-Time Detection Algorithm for Pipeline Defect Detection. Electronics, 14.","DOI":"10.3390\/electronics14010208"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Wen, Z., Liu, J., Shen, X., Lu, S., Zhao, H., and Wang, Q. (2023). A Lightweight Pipeline Defect Detection Method via Structural Reparameterization Technique and Ghost Convolution. Proceedings of the 2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS), Xiangtan, China, 12\u201314 May 2023, IEEE.","DOI":"10.1109\/DDCLS58216.2023.10166361"},{"key":"ref_15","first-page":"5059","article-title":"A Lightweight Pipeline Edge Detection Model Based on Heterogeneous Knowledge Distillation","volume":"71","author":"Zhu","year":"2024","journal-title":"IEEE Trans. Circuits Syst. II Express Briefs"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"786","DOI":"10.1007\/s11760-025-04386-z","article-title":"Lightweight Strategy of Pipeline Detection Model Based on Parameter Sharing, Pruning and Distillation","volume":"19","author":"Liu","year":"2025","journal-title":"Signal Image Video Process."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"075407","DOI":"10.1088\/1361-6501\/ade553","article-title":"Research on Lightweight Pipeline Defects Detection Algorithm Based on Attention Mechanism","volume":"36","author":"Liu","year":"2025","journal-title":"Meas. Sci. Technol."},{"key":"ref_18","unstructured":"Yang, L., Zhang, R.-Y., Li, L., and Xie, X. (2021, January 18\u201324). SimAM: A Simple, Parameter-Free Attention Module for Convolutional Neural Networks. Proceedings of the 38th International Conference on Machine Learning, Virtual Event."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Fu, H., Xue, R., Zhao, H., and Miao, C.H. (2025, January 15\u201317). Drainage pipeline defect detection method based on an improved YOLO11. Proceedings of the 2025 5th International Conference on Advanced Algorithms and Neural Networks, Qingdao, China.","DOI":"10.1109\/AANN66429.2025.11257598"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"113332","DOI":"10.1016\/j.asoc.2025.113332","article-title":"Diffusion-based road defect detection model integrating edge information and efficient multi-scale convolution","volume":"178","author":"Wang","year":"2025","journal-title":"Appl. Soft Comput."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Xu, S., Zheng, S., Xu, W., Xu, R., Wang, C., Zhang, J., Teng, X., Li, A., and Guo, L. (2024, January 15\u201319). HCF-Net: Hierarchical context fusion network for infrared small object detection. Proceedings of the 2024 IEEE International Conference on Multimedia and Expo, Niagara Falls, ON, Canada.","DOI":"10.1109\/ICME57554.2024.10687431"},{"key":"ref_22","first-page":"5906511","article-title":"EFLNet: Enhancing feature learning network for infrared small target detection","volume":"62","author":"Yang","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"10558","DOI":"10.1109\/TPAMI.2024.3447085","article-title":"A survey on deep neural network pruning: Taxonomy, comparison, analysis, and recommendations","volume":"46","author":"Cheng","year":"2024","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_24","unstructured":"Lee, J., Park, S., Mo, S., Ahn, S., and Shin, J. (2021, January 3\u20137). Layer-adaptive sparsity for the magnitude-based pruning. Proceedings of the 9th International Conference on Learning Representations, Virtual Event."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Qiu, Y., Niu, L., Sha, F., Cheng, Z., and Yanai, K. (2025). Entropy-guided search space optimization for efficient neural network pruning. Algorithms, 18.","DOI":"10.3390\/a18120736"},{"key":"ref_26","unstructured":"Hinton, G., Vinyals, O., and Dean, J. (2015). Distilling the knowledge in a neural network. arXiv."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Yang, L., Zhou, X., Li, X., Qiao, L., Li, Z., Yang, Z., Wang, G., and Li, X. (2023, January 2\u20136). Bridging cross-task protocol inconsistency for distillation in dense object detection. Proceedings of the IEEE International Conference on Computer Vision, Paris, France.","DOI":"10.1109\/ICCV51070.2023.01575"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., and Torralba, A. (2016, January 27\u201330). Learning deep features for discriminative localization. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.319"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra, D. (2017, January 22\u201329). Grad-CAM: Visual explanations from deep networks via gradient-based localization. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.74"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Chattopadhyay, A., Sarkar, A., Howlader, P., and Balasubramanian, V.N. (2018, January 12\u201315). Grad-CAM++: Generalized gradient-based visual explanations for deep convolutional networks. Proceedings of the 2018 IEEE Winter Conference on Applications of Computer Vision, Lake Tahoe, NV, USA.","DOI":"10.1109\/WACV.2018.00097"}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/17\/6\/613\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,29]],"date-time":"2026-06-29T04:13:48Z","timestamp":1782706428000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/17\/6\/613"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,21]]},"references-count":30,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2026,6]]}},"alternative-id":["info17060613"],"URL":"https:\/\/doi.org\/10.3390\/info17060613","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,21]]}}}