{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T21:34:21Z","timestamp":1775252061687,"version":"3.50.1"},"reference-count":56,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2025,6,1]],"date-time":"2025-06-01T00:00:00Z","timestamp":1748736000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,6,1]],"date-time":"2025-06-01T00:00:00Z","timestamp":1748736000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,6,1]],"date-time":"2025-06-01T00:00:00Z","timestamp":1748736000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2025,6,1]],"date-time":"2025-06-01T00:00:00Z","timestamp":1748736000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2025,6,1]],"date-time":"2025-06-01T00:00:00Z","timestamp":1748736000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2025,6,1]],"date-time":"2025-06-01T00:00:00Z","timestamp":1748736000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,6,1]],"date-time":"2025-06-01T00:00:00Z","timestamp":1748736000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100004735","name":"Natural Science Foundation of Hunan Province","doi-asserted-by":"publisher","award":["2023JJ50185"],"award-info":[{"award-number":["2023JJ50185"]}],"id":[{"id":"10.13039\/501100004735","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Applied Soft Computing"],"published-print":{"date-parts":[[2025,6]]},"DOI":"10.1016\/j.asoc.2025.113260","type":"journal-article","created":{"date-parts":[[2025,5,9]],"date-time":"2025-05-09T13:14:52Z","timestamp":1746796492000},"page":"113260","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":34,"special_numbering":"C","title":["A lightweight YOLOv8-based model with Squeeze-and-Excitation Version 2 for crack detection of pipelines"],"prefix":"10.1016","volume":"177","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0988-5792","authenticated-orcid":false,"given":"Zhaochao","family":"Li","sequence":"first","affiliation":[]},{"given":"Linxuan","family":"Xiao","sequence":"additional","affiliation":[]},{"given":"Meiling","family":"Shen","sequence":"additional","affiliation":[]},{"given":"Xiya","family":"Tang","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.asoc.2025.113260_bib1","series-title":"Climate change adaptation and canadian infrastructure","author":"Boyle","year":"2013"},{"key":"10.1016\/j.asoc.2025.113260_bib2","unstructured":"J.M. Perdek, R.D. Arnone, M.K. Stinson, M.E. Tuccillo, Managing urban watershed pathogen contamination, Cincinnati, Ohio (2003)."},{"key":"10.1016\/j.asoc.2025.113260_bib3","unstructured":"G. Hey, K. J\u00f6nsson, A. Mattsson, The impact of infiltration and inflow on wastewater treatment plants: A case study in Sweden, VA-Teknik Sodra, Rapport Nr (2016)."},{"issue":"2","key":"10.1016\/j.asoc.2025.113260_bib4","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1111\/1467-8667.00302","article-title":"Computer vision techniques for automatic structural assessment of underground pipes","volume":"18","author":"Sinha","year":"2003","journal-title":"Comput. -Aided Civ. Infrastruct. Eng."},{"issue":"2","key":"10.1016\/j.asoc.2025.113260_bib5","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1680\/wama.11.00039","article-title":"Sewer inspection and comparison of acoustic and CCTV methods","volume":"166","author":"Romanova","year":"2013","journal-title":"Proc. Inst. Civ. Eng. Water Manag."},{"issue":"3","key":"10.1016\/j.asoc.2025.113260_bib6","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1080\/15732479.2010.541265","article-title":"The consistency of visual sewer inspection data","volume":"9","author":"Dirksen","year":"2013","journal-title":"Struct. Infrastruct. Eng."},{"key":"10.1016\/j.asoc.2025.113260_bib7","first-page":"1565","article-title":"The pipeline defect assessment manual","author":"Cosham","year":"2002","journal-title":"Int. Pipeline Conf."},{"key":"10.1016\/j.asoc.2025.113260_bib8","series-title":"IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA'04. 2004","first-page":"4858","article-title":"An ultrasonic profiling method for sewer inspection","author":"Gomez","year":"2004"},{"key":"10.1016\/j.asoc.2025.113260_bib9","first-page":"1299","article-title":"Detecting Cracks in Pipelines Using Ultrascan CD","volume":"16A","author":"Uzelac","year":"1997"},{"issue":"2","key":"10.1016\/j.asoc.2025.113260_bib10","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1109\/JSEN.2002.1000245","article-title":"State of the art in sensor technologies for sewer inspection","volume":"2","author":"Duran","year":"2002","journal-title":"IEEE Sens. J."},{"issue":"5","key":"10.1016\/j.asoc.2025.113260_bib11","doi-asserted-by":"crossref","first-page":"587","DOI":"10.1016\/j.autcon.2008.12.003","article-title":"Jr, Automated defect detection for sewer pipeline inspection and condition assessment","volume":"18","author":"Guo","year":"2009","journal-title":"Autom. Constr."},{"issue":"2","key":"10.1016\/j.asoc.2025.113260_bib12","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/0266-8920(95)00030-5","article-title":"Applications of dynamic measurements to structural reliability updating","volume":"11","author":"de Lacalle","year":"1996","journal-title":"Probabilistic Eng. Mech."},{"issue":"2","key":"10.1016\/j.asoc.2025.113260_bib13","doi-asserted-by":"crossref","DOI":"10.1061\/(ASCE)IS.1943-555X.0000161","article-title":"Efficient algorithm for crack detection in sewer images from closed-circuit television inspections","volume":"20","author":"Halfawy","year":"2014","journal-title":"J. Infrastruct. Syst."},{"issue":"6","key":"10.1016\/j.asoc.2025.113260_bib14","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1111\/j.1467-8667.2006.00445.x","article-title":"Segmentation of pipe images for crack detection in buried sewers","volume":"21","author":"Iyer","year":"2006","journal-title":"Comput. -Aided Civ. Infrastruct. Eng."},{"issue":"02","key":"10.1016\/j.asoc.2025.113260_bib15","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1142\/S0219691311004055","article-title":"Feature extraction of sewer pipe defects using wavelet transform and co-occurrence matrix","author":"Yang","year":"2011","journal-title":"Int. J. Wavel. Multiresolution Inf. Process. 9"},{"issue":"1","key":"10.1016\/j.asoc.2025.113260_bib16","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.autcon.2005.02.007","article-title":"Segmentation of buried concrete pipe images","volume":"15","author":"Sinha","year":"2006","journal-title":"Autom. Constr."},{"issue":"3","key":"10.1016\/j.asoc.2025.113260_bib17","doi-asserted-by":"crossref","first-page":"1327","DOI":"10.1016\/j.eswa.2007.08.013","article-title":"Automated diagnosis of sewer pipe defects based on machine learning approaches","volume":"35","author":"Yang","year":"2008","journal-title":"Expert Syst. Appl."},{"issue":"6","key":"10.1016\/j.asoc.2025.113260_bib18","first-page":"822","article-title":"Images crack detection technology based on improved K-means algorithm","volume":"9","author":"Fang","year":"2014","journal-title":"J. Multimed."},{"key":"10.1016\/j.asoc.2025.113260_bib19","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1007\/s10044-013-0355-5","article-title":"Classification of defects with ensemble methods in the automated visual inspection of sewer pipes","volume":"18","author":"Wu","year":"2015","journal-title":"Pattern Anal. Appl."},{"key":"10.1016\/j.asoc.2025.113260_bib20","unstructured":"M. Browne, M. Dorn, R. Ouellette, T. Christaller, S. Shiry, C. Center, Wavelet entropy-based feature extraction for crack detection in sewer pipes, 6th International Conference on Mechatronics Technology, Kitakyushu, Japan, Citeseer, 2002."},{"key":"10.1016\/j.asoc.2025.113260_bib21","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1007\/978-1-4842-5364-9_6","article-title":"Convolutional neural networks","author":"Ketkar","year":"2021","journal-title":"Deep Learn. Python: Learn. Best. Pract. Deep Learn. Models PyTorch"},{"issue":"2","key":"10.1016\/j.asoc.2025.113260_bib22","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1007\/s13748-019-00203-0","article-title":"Convolutional neural network: a review of models, methodologies and applications to object detection","volume":"9","author":"Dhillon","year":"2020","journal-title":"Prog. Artif. Intell."},{"key":"10.1016\/j.asoc.2025.113260_bib23","unstructured":"H. Kataoka, K. Iwata, Y. Satoh, Feature evaluation of deep convolutional neural networks for object recognition and detection, arXiv preprint arXiv:1509.07627 (2015)."},{"key":"10.1016\/j.asoc.2025.113260_bib24","unstructured":"F. Visin, K. Kastner, K. Cho, M. Matteucci, A. Courville, Y. Bengio, Renet: A recurrent neural network based alternative to convolutional networks, arXiv preprint arXiv:1505.00393 (2015)."},{"key":"10.1016\/j.asoc.2025.113260_bib25","first-page":"779","article-title":"You only look once: unified, real-time object detection","author":"Redmon","year":"2016","journal-title":"Proc. IEEE Conf. Comput. Vis. Pattern Recognit."},{"issue":"2","key":"10.1016\/j.asoc.2025.113260_bib26","doi-asserted-by":"crossref","first-page":"484","DOI":"10.1177\/1475921720938486","article-title":"Imaging-based crack detection on concrete surfaces using You Only Look Once network","volume":"20","author":"Deng","year":"2021","journal-title":"Struct. Health Monit."},{"key":"10.1016\/j.asoc.2025.113260_bib27","series-title":"Computer Vision-ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part I 14","first-page":"21","article-title":"Ssd: Single shot multibox detector","author":"Liu","year":"2016"},{"key":"10.1016\/j.asoc.2025.113260_bib28","unstructured":"A.G. Howard, MobileNet: Ef\ufb01cient Convolutional Neural Networks for Mobile Vision Applications', arXiv preprint arXiv:1704.0486 (2017)."},{"key":"10.1016\/j.asoc.2025.113260_bib29","unstructured":"F.N. Iandola, SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and<\u20090.5 MB model size, arXiv preprint arXiv:1602.07360 (2016)."},{"key":"10.1016\/j.asoc.2025.113260_bib30","unstructured":"J. Redmon, Yolov3: An incremental improvement, arXiv preprint arXiv:1804.02767 (2018)."},{"key":"10.1016\/j.asoc.2025.113260_bib31","series-title":"2018 chinese automation congress (CAC)","first-page":"1563","article-title":"Rail surface defect detection method based on YOLOv3 deep learning networks","author":"Yanan","year":"2018"},{"key":"10.1016\/j.asoc.2025.113260_bib32","unstructured":"G. Jocher, A. Stoken, J. Borovec, L. Changyu, A. Hogan, L. Diaconu, F. Ingham, J. Poznanski, J. Fang, L. Yu, ultralytics\/yolov5: v3. 1-bug fixes and performance improvements, Zenodo (2020)."},{"key":"10.1016\/j.asoc.2025.113260_bib33","article-title":"Automatic defect detection in sewer pipe closed-circuit television images via improved you only look once version 5 object detection network","author":"Huang","year":"2024","journal-title":"IEEE Access"},{"issue":"3","key":"10.1016\/j.asoc.2025.113260_bib34","article-title":"FDSC-YOLOv8: advancements in automated crack identification for enhanced safety in underground engineering","volume":"140","author":"Wang","year":"2024","journal-title":"CMES-Comput. Model. Eng. Sci."},{"issue":"9","key":"10.1016\/j.asoc.2025.113260_bib35","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1007\/s10462-024-10877-1","article-title":"A comprehensive survey of deep learning-based lightweight object detection models for edge devices","volume":"57","author":"Mittal","year":"2024","journal-title":"Artif. Intell. Rev."},{"key":"10.1016\/j.asoc.2025.113260_bib36","doi-asserted-by":"crossref","DOI":"10.1109\/ACCESS.2024.3425166","article-title":"A comprehensive review of convolutional neural networks for defect detection in industrial applications","author":"Khanam","year":"2024","journal-title":"IEEE Access"},{"key":"10.1016\/j.asoc.2025.113260_bib37","doi-asserted-by":"crossref","DOI":"10.1016\/j.conbuildmat.2024.135025","article-title":"A novel YOLOv8-GAM-Wise-IoU model for automated detection of bridge surface cracks","volume":"414","author":"Xiong","year":"2024","journal-title":"Constr. Build. Mater."},{"issue":"18","key":"10.1016\/j.asoc.2025.113260_bib38","doi-asserted-by":"crossref","first-page":"6112","DOI":"10.3390\/s24186112","article-title":"Lightweight Sewer Pipe Crack Detection Method Based on Amphibious Robot and Improved YOLOv8n","volume":"24","author":"Lv","year":"2024","journal-title":"Sensors"},{"issue":"4","key":"10.1016\/j.asoc.2025.113260_bib39","doi-asserted-by":"crossref","first-page":"3733","DOI":"10.3390\/su15043733","article-title":"Lightweight network-based surface defect detection method for steel plates","volume":"15","author":"Wang","year":"2023","journal-title":"Sustainability"},{"key":"10.1016\/j.asoc.2025.113260_bib40","article-title":"An efficient deep neural network for surface defect detection in industrial edge sensing","author":"Wang","year":"2024","journal-title":"IEEE Trans. Ind. Inform."},{"key":"10.1016\/j.asoc.2025.113260_bib41","doi-asserted-by":"crossref","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."},{"issue":"2","key":"10.1016\/j.asoc.2025.113260_bib42","doi-asserted-by":"crossref","first-page":"3016","DOI":"10.3934\/mbe.2024134","article-title":"A surface defect detection method for steel pipe based on improved YOLO","volume":"21","author":"Wang","year":"2024","journal-title":"Math. Biosci. Eng. MBE"},{"key":"10.1016\/j.asoc.2025.113260_bib43","first-page":"1580","article-title":"Ghostnet: more features from cheap operations","author":"Han","year":"2020","journal-title":"Proc. IEEE CVF Conf. Comput. Vis. Pattern Recognit."},{"key":"10.1016\/j.asoc.2025.113260_bib44","article-title":"MSF-GhostNet: computationally-efficient YOLO for detecting drones in low-light conditions","author":"Misbah","year":"2024","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"10.1016\/j.asoc.2025.113260_bib45","unstructured":"M. Narayanan, SENetV2: Aggregated dense layer for channelwise and global representations. arXiv 2023, arXiv preprint arXiv:2311.10807."},{"key":"10.1016\/j.asoc.2025.113260_bib46","first-page":"390","article-title":"CSPNet: a new backbone that can enhance learning capability of CNN","author":"Wang","year":"2020","journal-title":"Proc. IEEE CVF Conf. Comput. Vis. Pattern Recognit. Workshops"},{"key":"10.1016\/j.asoc.2025.113260_bib47","first-page":"8759","article-title":"Path aggregation network for instance segmentation","author":"Liu","year":"2018","journal-title":"Proc. IEEE Conf. Comput. Vis. Pattern Recognit."},{"key":"10.1016\/j.asoc.2025.113260_bib48","series-title":"2024 7th International Conference on Computer Information Science and Application Technology (CISAT)","first-page":"36","article-title":"Lightweight PCB defect detection algorithm and deployment based on ASF-YOLO","author":"Li","year":"2024"},{"key":"10.1016\/j.asoc.2025.113260_bib49","series-title":"2024 9th International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)","first-page":"818","article-title":"Steel Defect Detection Based on Improved YOLOv8n","author":"Li","year":"2024"},{"key":"10.1016\/j.asoc.2025.113260_bib50","series-title":"2024 5th International Conference on Artificial Intelligence and Computer Engineering (ICAICE)","first-page":"1038","article-title":"HA-YOLO: A Real-Time Method for Detecting Surface Defects in Steel","author":"Wang","year":"2024"},{"key":"10.1016\/j.asoc.2025.113260_bib51","doi-asserted-by":"crossref","unstructured":"B. Koonce, B. Koonce, EfficientNet, Convolutional neural networks with swift for Tensorflow: image recognition and dataset categorization (2021) 109-123.","DOI":"10.1007\/978-1-4842-6168-2_10"},{"key":"10.1016\/j.asoc.2025.113260_bib52","first-page":"7132","article-title":"Squeeze-and-excitation networks","author":"Hu","year":"2018","journal-title":"Proc. IEEE Conf. Comput. Vis. Pattern Recognit."},{"key":"10.1016\/j.asoc.2025.113260_bib53","unstructured":"Y. Liu, Z. Shao, N. Hoffmann, Global attention mechanism: Retain information to enhance channel-spatial interactions, arXiv preprint arXiv:2112.05561 (2021)."},{"key":"10.1016\/j.asoc.2025.113260_bib54","first-page":"107984","article-title":"Yolov10: real-time end-to-end object detection","volume":"37","author":"Wang","year":"2024","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"1","key":"10.1016\/j.asoc.2025.113260_bib55","doi-asserted-by":"crossref","DOI":"10.1177\/1687814018819285","article-title":"Inspection scheduling based on reliability updating of gas turbine welded structures","volume":"11","author":"Coro","year":"2019","journal-title":"Adv. Mech. Eng."},{"key":"10.1016\/j.asoc.2025.113260_bib56","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2023.110773","article-title":"Early detection of tool wear in electromechanical broaching machines by monitoring main stroke servomotors","volume":"204","author":"Aldekoa","year":"2023","journal-title":"Mech. Syst. Signal Process."}],"container-title":["Applied Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S156849462500571X?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S156849462500571X?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T22:00:14Z","timestamp":1762552814000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S156849462500571X"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6]]},"references-count":56,"alternative-id":["S156849462500571X"],"URL":"https:\/\/doi.org\/10.1016\/j.asoc.2025.113260","relation":{},"ISSN":["1568-4946"],"issn-type":[{"value":"1568-4946","type":"print"}],"subject":[],"published":{"date-parts":[[2025,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A lightweight YOLOv8-based model with Squeeze-and-Excitation Version 2 for crack detection of pipelines","name":"articletitle","label":"Article Title"},{"value":"Applied Soft Computing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.asoc.2025.113260","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"113260"}}