{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T17:00:27Z","timestamp":1771952427178,"version":"3.50.1"},"reference-count":47,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2023,7,11]],"date-time":"2023-07-11T00:00:00Z","timestamp":1689033600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2022M712787"],"award-info":[{"award-number":["2022M712787"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["52178306"],"award-info":[{"award-number":["52178306"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2022M712787"],"award-info":[{"award-number":["2022M712787"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52178306"],"award-info":[{"award-number":["52178306"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Cracks are one of the safety-evaluation indicators for structures, providing a maintenance basis for the health and safety of structures in service. Most structural inspections rely on visual observation, while bridges rely on traditional methods such as bridge inspection vehicles, which are inefficient and pose safety risks. To alleviate the problem of low efficiency and the high cost of structural health monitoring, deep learning, as a new technology, is increasingly being applied to crack detection and recognition. Focusing on this, the current paper proposes an improved model based on the attention mechanism and the U-Net network for crack-identification research. First, the training results of the two original models, U-Net and lrassp, were compared in the experiment. The results showed that U-Net performed better than lrassp according to various indicators. Therefore, we improved the U-Net network with the attention mechanism. After experimenting with the improved network, we found that the proposed ECA-UNet network increased the Intersection over Union (IOU) and recall indicators compared to the original U-Net network by 0.016 and 0.131, respectively. In practical large-scale structural crack recognition, the proposed model had better recognition performance than the other two models, with almost no errors in identifying noise under the premise of accurately identifying cracks, demonstrating a stronger capacity for crack recognition.<\/jats:p>","DOI":"10.3390\/s23146295","type":"journal-article","created":{"date-parts":[[2023,7,12]],"date-time":"2023-07-12T01:05:01Z","timestamp":1689123901000},"page":"6295","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Modification and Evaluation of Attention-Based Deep Neural Network for Structural Crack Detection"],"prefix":"10.3390","volume":"23","author":[{"given":"Hangming","family":"Yuan","sequence":"first","affiliation":[{"name":"Polytechnic Institute, Zhejiang University, Hangzhou 310058, China"}]},{"given":"Tao","family":"Jin","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, Zhejiang University, Hangzhou 310058, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0012-5842","authenticated-orcid":false,"given":"Xiaowei","family":"Ye","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, Zhejiang University, Hangzhou 310058, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Hamishebahar, Y., Guan, H., So, S., and Jo, J. (2022). A comprehensive review of deep learning-based crack detection approaches. Appl. Sci., 12.","DOI":"10.3390\/app12031374"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Munawar, H.S., Hammad, A.W.A., Haddad, A., Soares, C.A.P., and Waller, S.T. (2021). Image-based crack detection methods: A review. Infrastructures, 6.","DOI":"10.3390\/infrastructures6080115"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.autcon.2019.04.005","article-title":"Computer vision-based concrete crack detection using U-net fully convolutional networks","volume":"104","author":"Liu","year":"2019","journal-title":"Autom. Constr."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1111\/mice.12263","article-title":"Deep learning-based crack damage detection using convolutional neural networks","volume":"32","author":"Cha","year":"2017","journal-title":"Comput.-Aided Civ. Infrastruct. Eng."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Azimi, M., Eslamlou, A.D., and Pekcan, G. (2020). Data-driven structural health monitoring and damage detection through deep learning: State-of-the-art review. Sensors, 20.","DOI":"10.3390\/s20102778"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"107077","DOI":"10.1016\/j.ymssp.2020.107077","article-title":"A review of vibration-based damage detection in civil structures: From traditional methods to Machine Learning and Deep Learning applications","volume":"147","author":"Avci","year":"2021","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1906","DOI":"10.1177\/14759217211036880","article-title":"Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights","volume":"21","author":"Malekloo","year":"2022","journal-title":"Struct. Health Monit."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"7240129","DOI":"10.1155\/2020\/7240129","article-title":"Concrete Cracks Detection Using Convolutional NeuralNetwork Based on Transfer Learning","volume":"2020","author":"Su","year":"2020","journal-title":"Math. Probl. Eng."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Kim, J.J., Kim, A.R., and Lee, S.W. (2020). Artificial neural network-based automated crack detection and analysis for the inspection of concrete structures. Appl. Sci., 10.","DOI":"10.3390\/app10228105"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"6520620","DOI":"10.1155\/2019\/6520620","article-title":"Image-based concrete crack detection using convolutional neural network and exhaustive search technique","volume":"2019","author":"Li","year":"2019","journal-title":"Adv. Civ. Eng."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"101105","DOI":"10.1016\/j.aei.2020.101105","article-title":"Anomaly detection of defects on concrete structures with the convolutional autoencoder","volume":"45","author":"Chow","year":"2020","journal-title":"Adv. Eng. Inform."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3412","DOI":"10.1177\/1369433219836292","article-title":"Structural crack detection using deep learning\u2013based fully convolutional networks","volume":"22","author":"Ye","year":"2019","journal-title":"Adv. Struct. Eng."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"104712","DOI":"10.1016\/j.autcon.2022.104712","article-title":"Unifying transformer and convolution for dam crack detection","volume":"147","author":"Zhang","year":"2023","journal-title":"Autom. Constr."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1031","DOI":"10.1016\/j.conbuildmat.2018.08.011","article-title":"Comparison of deep convolutional neural networks and edge detectors for image-based crack detection in concrete","volume":"186","author":"Dorafshan","year":"2018","journal-title":"Constr. Build. Mater."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"110157","DOI":"10.1016\/j.engstruct.2019.110157","article-title":"Increasing the robustness of material-specific deep learning models for crack detection across different materials","volume":"206","author":"Alipour","year":"2020","journal-title":"Eng. Struct."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"107474","DOI":"10.1016\/j.patcog.2020.107474","article-title":"A novel hybrid approach for crack detection","volume":"107","author":"Fang","year":"2020","journal-title":"Pattern Recognit."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"24452","DOI":"10.1109\/ACCESS.2018.2829347","article-title":"Automatic pixel-level pavement crack detection using information of multi-scale neighborhoods","volume":"6","author":"Ai","year":"2018","journal-title":"IEEE Access"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Feng, C., Zhang, H., Wang, H., Wang, S., and Li, Y. (2020). Automatic pixel-level crack detection on dam surface using deep convolutional network. Sensors, 20.","DOI":"10.3390\/s20072069"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"103989","DOI":"10.1016\/j.autcon.2021.103989","article-title":"Structural crack detection using deep convolutional neural networks","volume":"133","author":"Ali","year":"2022","journal-title":"Autom. Constr."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","article-title":"Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs","volume":"40","author":"Chen","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","article-title":"Segnet: A deep convolutional encoder-decoder architecture for image segmentation","volume":"39","author":"Badrinarayanan","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015, January 7\u201312). Fully convolutional networks for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"731","DOI":"10.1111\/mice.12334","article-title":"Autonomous structural visual inspection using region-based deep learning for detecting multiple damage types","volume":"33","author":"Cha","year":"2018","journal-title":"Comput.-Aided Civ. Infrastruct. Eng."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"616","DOI":"10.1111\/mice.12433","article-title":"Automatic pixel-level multiple damage detection of concrete structure using fully convolutional network","volume":"34","author":"Li","year":"2019","journal-title":"Comput.-Aided Civ. Infrastruct. Eng."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"107237","DOI":"10.1016\/j.engfailanal.2023.107237","article-title":"Physical interpretation of machine learning-based recognition of defects for the risk management of existing bridge heritage","volume":"149","author":"Cardellicchio","year":"2023","journal-title":"Eng. Fail. Anal."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"951","DOI":"10.1111\/mice.12477","article-title":"Concrete crack detection using context-aware deep semantic segmentation network","volume":"34","author":"Zhang","year":"2019","journal-title":"Comput.-Aided Civ. Infrastruct. Eng."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"493","DOI":"10.3151\/jact.18.493","article-title":"Crack detection from a concrete surface image based on semantic segmentation using deep learning","volume":"18","author":"Yamane","year":"2020","journal-title":"J. Adv. Concr. Technol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1007\/s42405-018-0120-5","article-title":"Robust concrete crack detection using deep learning-based semantic segmentation","volume":"20","author":"Lee","year":"2019","journal-title":"Int. J. Aeronaut. Space Sci."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"51446","DOI":"10.1109\/ACCESS.2020.2980086","article-title":"Semi-supervised semantic segmentation using adversarial learning for pavement crack detection","volume":"8","author":"Li","year":"2020","journal-title":"IEEE Access"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"82031","DOI":"10.1109\/ACCESS.2021.3086020","article-title":"U-net and its variants for medical image segmentation: A review of theory and applications","volume":"9","author":"Siddique","year":"2021","journal-title":"IEEE Access"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"82744","DOI":"10.1109\/ACCESS.2019.2923753","article-title":"An improved u-net convolutional networks for seabed mineral image segmentation","volume":"7","author":"Song","year":"2019","journal-title":"IEEE Access"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"104699","DOI":"10.1016\/j.compbiomed.2021.104699","article-title":"Sharp U-Net: Depthwise convolutional network for biomedical image segmentation","volume":"136","author":"Zunair","year":"2021","journal-title":"Comput. Biol. Med."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1016\/j.egyr.2021.10.037","article-title":"Improved U-Net based insulator image segmentation method based on attention mechanism","volume":"7","author":"Han","year":"2021","journal-title":"Energy Rep."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1080\/22797254.2021.2018944","article-title":"Building extraction from remote sensing images using deep residual U-Net","volume":"55","author":"Wang","year":"2022","journal-title":"Eur. J. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1007\/s41095-022-0271-y","article-title":"Attention mechanisms in computer vision: A survey","volume":"8","author":"Guo","year":"2022","journal-title":"Comput. Vis. Media"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Li, C., Fu, L., Zhu, Q., Zhu, J., Fang, Z., Xie, Y., Guo, Y., and Gong, Y. (2021). Attention enhanced u-net for building extraction from farmland based on google and worldview-2 remote sensing images. Remote Sens., 13.","DOI":"10.3390\/rs13214411"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"540","DOI":"10.1109\/TMI.2018.2867261","article-title":"Recalibrating fully convolutional networks with spatial and channel \u201csqueeze and excitation\u201d blocks","volume":"38","author":"Roy","year":"2018","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Wang, L., Peng, J., and Sun, W. (2019). Spatial\u2013spectral squeeze-and-excitation residual network for hyperspectral image classification. Remote Sens., 11.","DOI":"10.3390\/rs11070884"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Chen, B., Zhang, Z., Liu, N., Tan, Y., Liu, X., and Chen, T. (2020). Spatiotemporal convolutional neural network with convolutional block attention module for micro-expression recognition. Information, 11.","DOI":"10.3390\/info11080380"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"905","DOI":"10.1109\/LGRS.2020.2988294","article-title":"SCAttNet: Semantic segmentation network with spatial and channel attention mechanism for high-resolution remote sensing images","volume":"18","author":"Li","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Xu, G., Han, X., Zhang, Y., and Wu, C. (2022). Dam crack image detection model on feature enhancement and attention mechanism. Water, 15.","DOI":"10.3390\/w15010064"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1859","DOI":"10.1177\/1369433220986638","article-title":"Intelligent crack detection based on attention mechanism in convolution neural network","volume":"24","author":"Cui","year":"2021","journal-title":"Adv. Struct. Eng."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Ren, J., Zhao, G., Ma, Y., Zhao, D., Liu, T., and Yan, J. (2022). Automatic Pavement Crack Detection Fusing Attention Mechanism. Electronics, 11.","DOI":"10.3390\/electronics11213622"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1914","DOI":"10.1111\/mice.12881","article-title":"Tiny-Crack-Net: A multiscale feature fusion network with attention mechanisms for segmentation of tiny cracks","volume":"37","author":"Chu","year":"2022","journal-title":"Comput.-Aided Civ. Infrastruct. Eng."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Liu, T., Luo, R., Xu, L., Feng, D., Cao, L., Liu, S., and Guo, J. (2022). Spatial Channel Attention for Deep Convolutional Neural Networks. Mathematics, 10.","DOI":"10.3390\/math10101750"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"04721008","DOI":"10.1061\/(ASCE)ST.1943-541X.0003140","article-title":"Structural crack detection from benchmark data sets using pruned fully convolutional networks","volume":"147","author":"Ye","year":"2021","journal-title":"J. Struct. Eng."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/14\/6295\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:10:30Z","timestamp":1760127030000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/14\/6295"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,11]]},"references-count":47,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2023,7]]}},"alternative-id":["s23146295"],"URL":"https:\/\/doi.org\/10.3390\/s23146295","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,11]]}}}