An integrated edge-gated and multi-scale spatial attention model for drainage pipeline defect segmentation

doi:10.13474/j.cnki.11-2246.2025.0617

Abstract

Abstract: The aging of urban underground drainage pipelines has led to frequent occurrences of pipe damage, blockages, and other issues. Manual inspection methods struggle to meet the growing demand for detection, while existing intelligent detection technologies still have room for improvement in achieving higher intersection over union(IoU)when addressing irregularly shaped defects and intricate boundaries. This study proposes a semantic segmentation model for drainage pipelines named PGGNet to enhance defect boundary recognition performance. The gated edge attention model(GEA) in PGGNet integrates edge features through the Laplacian edge detection algorithm, significantly improving the ability to capture defect boundaries. Meanwhile, the multi-scale guided spatial attention-state space model(MGSA-SSM)combines a state-space model with MGSA of multi-scale mechanisms to guide the model in capturing both global contours and local details of defects across different scales, thereby enhancing the recognition capability for complex boundaries. Experimental results demonstrate that PGGNet outperforms mainstream algorithms, achieving an mPA of 94.32% and an mIoU of 93.08%, which meets the requirements for automated defect detection in drainage pipelines.

Key words: urban underground drainage pipelines, defect detection, PGGNet, gated mechanism, multi-scale guided spatial attention model

CLC Number:

P258

CHEN Dengfeng, ZHAO Hanghui, LIU Shipeng, MENG Tunliang, WANG Zepeng. An integrated edge-gated and multi-scale spatial attention model for drainage pipeline defect segmentation[J]. Bulletin of Surveying and Mapping, 2025, 0(6): 97-102.

References

[1] HAURUM J B,MOESLUND T B.A survey on image-based automation of CCTV and SSET sewer inspections[J].Automation in Construction,2020,111:103061.
[2] 刘春明.城市道路塌陷成因分析与对策[J].城市勘测,2018(S1):184-187.
[3] XU Zuxin,XU Jin,YIN Hailong,et al.Urban river pollution control in developing countries[J].Nature Sustainability,2019,2:158-160.
[4] KUMAR S S,ABRAHAM D M,JAHANSHAHI M R,et al.Automated defect classification in sewer closed circuit television inspections using deep convolutional neural networks[J].Automation in Construction,2018,91:273-283.
[5] 王俊岭,邓玉莲,李英,等.排水管道检测与缺陷识别技术综述[J].科学技术与工程,2020,20(33):13520-13528.
[6] 许铁林,廖立国,周昌林.基于深度学习的复杂城市排水管道缺陷智能识别系统[J].测绘通报,2024(S2):37-41.
[7] 苏常旺,胡少伟,张海丰,等.基于改进YOLOX算法的给水管道内缺陷智能识别与定位[J].测绘通报,2023(12):70-75.
[8] YUSSUF A N,WEERASINGHE N P,CHEN Haosen,et al.Leveraging deep learning techniques for condition assessment of stormwater pipe network[J].Journal of Civil Structural Health Monitoring,2025,15(2):619-633.
[9] 王守志,刘章,王冬,等.基于PP-YOLOE的城市排水管网缺陷检测及应用[J].中国给水排水,2024,40(18):130-136.
[10] 周倩倩,刘汉林,陈维锋,等.基于DeepLabV3+的排水管道缺陷检测与语义分割[J].中国给水排水,2022,38(13):22-27.
[11] PAN Gang,ZHENG Yaoxian,GUO Shuai,et al.Automatic sewer pipe defect semantic segmentation based on improved U-Net[J].Automation in Construction,2020,119:103383.
[12] LI Mingze,LI Mingchao,REN Qiubing,et al.PipeTransUNet:CNN and Transformer fusion network for semantic segmentation and severity quantification of multiple sewer pipe defects[J].Applied Soft Computing,2024,159:111673.
[13] MATTSSON J,HEDSTRÖM A,VIKLANDER M,et al.Fat,oil,and grease accumulation in sewer systems:comprehensive survey of experiences of Scandinavian municipalities[J].Journal of Environmental Engineering,2014,140(3):04014003.
[14] HAURUM J B,MADADI M,ESCALERA S,et al.Multi-scale hybrid vision transformer and Sinkhorn tokenizer for sewer defect classification[J].Automation in Construction,2022,144:104614.
[15] DANG L M,WANG Hanxiang,LI Yanfen,et al.DefectTR:end-to-end defect detection for sewage networks using a transformer[J].Construction and Building Materials,2022,325:126584.
[16] ZHU Lianghui,LIAO Bencheng,ZHANG Qian,et al.Vision mamba:efficient visual representation learning with bidirectional state space model[EB/OL].[2024-10-20].https://arxiv.org/abs/2401.09417v3.
[17] BURT P J,ADELSON E H.The Laplacian pyramid as a compact image code[M]//Readings in Computer Vision.Amsterdam:Elsevier,1987:671-679.
[18] HAURUM J B,MOESLUND T B.Sewer-ML:a multi-label sewer defect classification dataset and benchmark[C]//Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition.Nashville,TN:IEEE,2021:13456-13467.