{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T20:32:03Z","timestamp":1776371523248,"version":"3.51.2"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2025,1,28]],"date-time":"2025-01-28T00:00:00Z","timestamp":1738022400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,28]],"date-time":"2025-01-28T00:00:00Z","timestamp":1738022400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SIViP"],"published-print":{"date-parts":[[2025,3]]},"DOI":"10.1007\/s11760-025-03846-w","type":"journal-article","created":{"date-parts":[[2025,1,28]],"date-time":"2025-01-28T18:31:20Z","timestamp":1738089080000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Automated intelligent detection system for bridge damages with Fractal-features-based improved YOLOv7"],"prefix":"10.1007","volume":"19","author":[{"given":"Yongjian","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xing","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenbin","family":"Yan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,1,28]]},"reference":[{"key":"3846_CR1","doi-asserted-by":"publisher","first-page":"132839","DOI":"10.1016\/j.conbuildmat.2023.132839","volume":"400","author":"TS Tran","year":"2023","unstructured":"Tran, T.S., Nguyen, S.D., Lee, H.J., Tran, V.P.: Advanced crack detection and segmentation on bridge decks using deep learning. Constr. Build. Mater. 400, 132839 (2023)","journal-title":"Constr. Build. Mater."},{"key":"3846_CR2","first-page":"18","volume":"30","author":"S He","year":"2017","unstructured":"He, S., et al.: Overview of highway bridge inspection and evaluation technologies. Chin. J. Highway Transp. 30, 18 (2017)","journal-title":"Chin. J. Highway Transp."},{"key":"3846_CR3","doi-asserted-by":"publisher","first-page":"759","DOI":"10.1111\/mice.12141","volume":"30","author":"CM Yeum","year":"2015","unstructured":"Yeum, C.M., Dyke, S.J.: Vision-based automated crack detection for bridge inspection. Comput. -Aided Civ. Infrastr. Eng. 30, 759\u2013770 (2015). https:\/\/doi.org\/10.1111\/mice.12141","journal-title":"Comput. -Aided Civ. Infrastr. Eng."},{"key":"3846_CR4","doi-asserted-by":"publisher","first-page":"731","DOI":"10.1111\/mice.12334","volume":"33","author":"Y-J Cha","year":"2018","unstructured":"Cha, Y.-J., Choi, W., Suh, G., Mahmoudkhani, S., B\u00fcy\u00fck\u00f6zt\u00fcrk, O.: Autonomous structural visual inspection using region-based deep learning for detecting multiple damage types. Comput. -Aided Civ. Infrastr. Eng. 33, 731\u2013747 (2018). https:\/\/doi.org\/10.1111\/mice.12334","journal-title":"Comput. -Aided Civ. Infrastr. Eng."},{"key":"3846_CR5","first-page":"121","volume":"23\u201327","author":"M Wang","year":"2019","unstructured":"Wang, M.: Research on bridge damage detection model based on attention mechanism and wavelet transform. Appl. Sci. 23\u201327, 121\u2013125 (2019)","journal-title":"Appl. Sci."},{"key":"3846_CR6","doi-asserted-by":"publisher","first-page":"389","DOI":"10.1111\/mice.12500","volume":"35","author":"C Zhang","year":"2020","unstructured":"Zhang, C., Chang, C.-C., Jamshidi, M.: Concrete bridge surface damage detection using a single-stage detector. Comput.-Aided Civ. Infrastr. Eng. 35, 389\u2013409 (2020). https:\/\/doi.org\/10.1111\/mice.12500","journal-title":"Comput.-Aided Civ. Infrastr. Eng."},{"key":"3846_CR7","first-page":"10","volume":"18","author":"J Zou","year":"2021","unstructured":"Zou, J., et al.: Bridge appearance damage identification based on improved yolo v3 algorithm in complex backgrounds. J. Railway Sci. Eng. 18, 10 (2021)","journal-title":"J. Railway Sci. Eng."},{"key":"3846_CR8","doi-asserted-by":"publisher","DOI":"10.1061\/(asce)cp.1943-5487.0000954","author":"JJ Lin","year":"2021","unstructured":"Lin, J.J., Ibrahim, A., Sarwade, S., Golparvar-Fard, M.: Bridge inspection with aerial robots: automating the entire pipeline of visual data capture, 3d mapping, defect detection, analysis, and reporting. J. Comput. Civ. Eng. (2021). https:\/\/doi.org\/10.1061\/(asce)cp.1943-5487.0000954","journal-title":"J. Comput. Civ. Eng."},{"key":"3846_CR9","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y. et\u00a0al. Feature pyramid networks for object detection. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)","DOI":"10.1109\/CVPR.2017.106"},{"key":"3846_CR10","doi-asserted-by":"publisher","DOI":"10.1631\/jzus.a2200175","author":"Z Mu","year":"2023","unstructured":"Mu, Z., et al.: Adaptive cropping shallow attention network for defect detection of bridge girder steel using unmanned aerial vehicle images. J. Zhejiang University-Sci A (2023). https:\/\/doi.org\/10.1631\/jzus.a2200175","journal-title":"J. Zhejiang University-Sci A"},{"key":"3846_CR11","unstructured":"Pei, C. et\u00a0al. Utilization of unmanned aerial vehicle, artificial intelligence, and remote measurement technology for bridge inspections. Journal of robotics and mechatronics,Journal of robotics and mechatronics (2020)"},{"key":"3846_CR12","doi-asserted-by":"publisher","first-page":"104666","DOI":"10.1016\/j.autcon.2022.104666","volume":"146","author":"C-Y Liu","year":"2023","unstructured":"Liu, C.-Y., Chou, J.-S.: Bayesian-optimized deep learning model to segment deterioration patterns underneath bridge decks photographed by unmanned aerial vehicle. Autom. Constr. 146, 104666 (2023)","journal-title":"Autom. Constr."},{"key":"3846_CR13","volume-title":"Attention is all you need","author":"A Vaswani","year":"2017","unstructured":"Vaswani, A., et al.: Attention is all you need. Neural Information Processing Systems, Neural Information Processing Systems (2017)"},{"key":"3846_CR14","doi-asserted-by":"publisher","first-page":"104929","DOI":"10.1016\/j.autcon.2023.104929","volume":"152","author":"W Ding","year":"2023","unstructured":"Ding, W., Yang, H., Yu, K., Shu, J.: Crack detection and quantification for concrete structures using uav and transformer. Autom. Constr. 152, 104929 (2023)","journal-title":"Autom. Constr."},{"key":"3846_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.119019","volume-title":"A novel transformer model for surface damage detection and cognition of concrete bridges","author":"H Wan","year":"2023","unstructured":"Wan, H., et al.: A novel transformer model for surface damage detection and cognition of concrete bridges. Expert Systems With Applications, Expert Systems With Applications (2023)"},{"key":"3846_CR16","doi-asserted-by":"publisher","first-page":"116913","DOI":"10.1016\/j.engstruct.2023.116913","volume":"296","author":"Y Lan","year":"2023","unstructured":"Lan, Y., et al.: Bridge frequency identification in city bus monitoring: a coherence-ppi algorithm. Eng. Struct. 296, 116913 (2023)","journal-title":"Eng. Struct."},{"key":"3846_CR17","doi-asserted-by":"publisher","first-page":"110899","DOI":"10.1016\/j.ymssp.2023.110899","volume":"206","author":"Y Lan","year":"2024","unstructured":"Lan, Y., Li, Z., Lin, W.: Physics-guided diagnosis framework for bridge health monitoring using raw vehicle accelerations. Mecha. Syst. Signal Process. 206, 110899 (2024)","journal-title":"Mecha. Syst. Signal Process."},{"key":"3846_CR18","first-page":"1","volume":"16","author":"F Toriumi","year":"2023","unstructured":"Toriumi, F., Bittencourt, T., Futai, M.: Uav-based inspection of bridge and tunnel structures: an application review. Revista IBRACON de Estruturas e Mater. 16, 1\u201319 (2023)","journal-title":"Revista IBRACON de Estruturas e Mater."},{"key":"3846_CR19","doi-asserted-by":"publisher","first-page":"104273","DOI":"10.1016\/j.autcon.2022.104273","volume":"139","author":"Y Tian","year":"2022","unstructured":"Tian, Y., Chen, C., Sagoe-Crentsil, K., Zhang, J., Duan, W.: Intelligent robotic systems for structural health monitoring: applications and future trends. Automat. Constr. 139, 104273 (2022)","journal-title":"Automat. Constr."},{"key":"3846_CR20","doi-asserted-by":"publisher","first-page":"118094","DOI":"10.1016\/j.engstruct.2024.118094","volume":"310","author":"H Ju","year":"2024","unstructured":"Ju, H., Shi, H., Shen, W., Deng, Y.: An accurate and low-cost vehicle-induced deflection prediction framework for long-span bridges using deep learning and monitoring data. Eng. Struct. 310, 118094 (2024)","journal-title":"Eng. Struct."},{"key":"3846_CR21","doi-asserted-by":"publisher","first-page":"e2618","DOI":"10.1002\/stc.2618","volume":"27","author":"H Zhao","year":"2020","unstructured":"Zhao, H., Ding, Y., Li, A., Sheng, W., Geng, F.: Digital modeling on the nonlinear mapping between multi-source monitoring data of in-service bridges. Struct. Control. Health Monit. 27, e2618 (2020)","journal-title":"Struct. Control. Health Monit."},{"key":"3846_CR22","doi-asserted-by":"publisher","DOI":"10.1002\/stc.3113","volume":"29","author":"Y Deng","year":"2022","unstructured":"Deng, Y., Ju, H., Zhai, W., Li, A., Ding, Y.: Correlation model of deflection, vehicle load, and temperature for in-service bridge using deep learning and structural health monitoring. Struct. Control. Health Monit. 29, e3113 (2022). https:\/\/doi.org\/10.1002\/stc.3113","journal-title":"Struct. Control. Health Monit."},{"key":"3846_CR23","doi-asserted-by":"publisher","first-page":"05021004","DOI":"10.1061\/(ASCE)BE.1943-5592.0001716","volume":"26","author":"Z Xiang Yue","year":"2021","unstructured":"Xiang Yue, Z., Liang Ding, Y., Wei Zhao, H.: Deep learning-based minute-scale digital prediction model of temperature-induced deflection of a cable-stayed bridge: Case study. J. Bridge Eng. 26, 05021004 (2021)","journal-title":"J. Bridge Eng."},{"key":"3846_CR24","doi-asserted-by":"publisher","first-page":"244","DOI":"10.1016\/j.autcon.2018.07.003","volume":"94","author":"H Peel","year":"2018","unstructured":"Peel, H., Luo, S., Cohn, A., Fuentes, R.: Localisation of a mobile robot for bridge bearing inspection. Autom. Constr. 94, 244\u2013256 (2018). https:\/\/doi.org\/10.1016\/j.autcon.2018.07.003","journal-title":"Autom. Constr."},{"key":"3846_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2018.02.013","author":"B Sutter","year":"2018","unstructured":"Sutter, B., et al.: A semi-autonomous mobile robot for bridge inspection. Automat. Constr. (2018). https:\/\/doi.org\/10.1016\/j.autcon.2018.02.013","journal-title":"Automat. Constr."},{"key":"3846_CR26","doi-asserted-by":"publisher","first-page":"549","DOI":"10.1111\/mice.12519","volume":"35","author":"S Jiang","year":"2020","unstructured":"Jiang, S., Zhang, J.: Real-time crack assessment using deep neural networks with wall-climbing unmanned aerial system. Comput. -Aided Civ. Infrastr. Eng. 35, 549\u2013564 (2020). https:\/\/doi.org\/10.1111\/mice.12519","journal-title":"Comput. -Aided Civ. Infrastr. Eng."},{"key":"3846_CR27","doi-asserted-by":"publisher","unstructured":"Nguyen, S.\u00a0T., La, H.\u00a0M.: A climbing robot for steel bridge inspection. Journal of Intelligent; Robotic Systems (2021). https:\/\/doi.org\/10.1007\/s10846-020-01266-1","DOI":"10.1007\/s10846-020-01266-1"},{"key":"3846_CR28","doi-asserted-by":"publisher","DOI":"10.1109\/tmech.2016.2614578","author":"KH Cho","year":"2017","unstructured":"Cho, K.H., et al.: Multifunctional robotic crawler for inspection of suspension bridge hanger cables: mechanism design and performance validation. IEEE\/ASME Transact. Mech. (2017). https:\/\/doi.org\/10.1109\/tmech.2016.2614578","journal-title":"IEEE\/ASME Transact. Mech."},{"key":"3846_CR29","doi-asserted-by":"publisher","first-page":"04020064","DOI":"10.1061\/(ASCE)CP.1943-5487.0000954","volume":"35","author":"JJ Lin","year":"2021","unstructured":"Lin, J.J., Ibrahim, A., Sarwade, S., Golparvar-Fard, M.: Bridge inspection with aerial robots: Automating the entire pipeline of visual data capture, 3d mapping, defect detection, analysis, and reporting. J. Comput. Civ. Eng. 35, 04020064 (2021)","journal-title":"J. Comput. Civ. Eng."},{"key":"3846_CR30","doi-asserted-by":"crossref","unstructured":"Jung, S., Choi, D., Song, S., Myung, H.: Bridge inspection using unmanned aerial vehicle based on hg-slam: Hierarchical graph-based slam. Remote Sensing12 (2020). https:\/\/www.mdpi.com\/2072-4292\/12\/18\/3022","DOI":"10.3390\/rs12183022"},{"key":"3846_CR31","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1016\/j.autcon.2018.10.006","volume":"97","author":"G Morgenthal","year":"2019","unstructured":"Morgenthal, G., et al.: Framework for automated uas-based structural condition assessment of bridges. Automat. Construct. 97, 77\u201395 (2019)","journal-title":"Automat. Construct."},{"key":"3846_CR32","doi-asserted-by":"publisher","first-page":"67","DOI":"10.3390\/drones4040067","volume":"4","author":"A Humpe","year":"2020","unstructured":"Humpe, A.: Bridge inspection with an off-the-shelf 360 camera drone. Drones 4, 67 (2020)","journal-title":"Drones"},{"key":"3846_CR33","doi-asserted-by":"publisher","first-page":"244","DOI":"10.1016\/j.autcon.2018.07.003","volume":"94","author":"H Peel","year":"2018","unstructured":"Peel, H., Luo, S., Cohn, A., Fuentes, R.: Localisation of a mobile robot for bridge bearing inspection. Autom. Constr. 94, 244\u2013256 (2018)","journal-title":"Autom. Constr."},{"key":"3846_CR34","doi-asserted-by":"publisher","first-page":"3022","DOI":"10.3390\/rs12183022","volume":"12","author":"S Jung","year":"2020","unstructured":"Jung, S., Choi, D., Song, S., Myung, H.: Bridge inspection using unmanned aerial vehicle based on hg-slam: Hierarchical graph-based slam. Remote Sens. 12, 3022 (2020)","journal-title":"Remote Sens."},{"key":"3846_CR35","doi-asserted-by":"publisher","first-page":"624","DOI":"10.1108\/ci-10-2021-0201","volume":"22","author":"S Talebi","year":"2022","unstructured":"Talebi, S., Wu, S., Al-Adhami, M., Shelbourn, M., Serugga, J.: The development of a digitally enhanced visual inspection framework for masonry bridges in the UK. Constr. Innov. 22, 624\u2013646 (2022). https:\/\/doi.org\/10.1108\/ci-10-2021-0201","journal-title":"Constr. Innov."},{"key":"3846_CR36","doi-asserted-by":"publisher","first-page":"27","DOI":"10.5055\/jem.2020.0448","volume":"18","author":"CA Baker","year":"2020","unstructured":"Baker, C.A., Rapp, R.R., Elwakil, E., Zhang, J.: Infrastructure assessment post-disaster: remotely sensing bridge structural damage by unmanned aerial vehicle in low-light conditions. J Emerg. Manag. 18, 27\u201341 (2020)","journal-title":"J Emerg. Manag."},{"key":"3846_CR37","doi-asserted-by":"publisher","unstructured":"Nguyen, S.\u00a0T., La, H.\u00a0M.: A climbing robot for steel bridge inspection. Journal of Intelligent; Robotic Systems (2021). https:\/\/doi.org\/10.1007\/s10846-020-01266-1","DOI":"10.1007\/s10846-020-01266-1"},{"key":"3846_CR38","first-page":"1","volume":"37","author":"Y Liu","year":"2024","unstructured":"Liu, Y., Feng, C., Chen, W., Fan, J.: A review of research on bridge superficial disease detection based on machine vision method (in Chinese). Chin. Highway J. 37, 1\u201315 (2024)","journal-title":"Chin. Highway J."},{"key":"3846_CR39","first-page":"26","volume":"52","author":"D Gong","year":"2024","unstructured":"Gong, D., Hu, H., Wang, S., Xia, L., Fang, Q.: Research on foundation suitability of highway and bridge in karst complex development area-taking chishi special bridge as an example(in chinese). Eng. Survey 52, 26\u201332 (2024)","journal-title":"Eng. Survey"},{"key":"3846_CR40","unstructured":"Le\u00a0M\u00e9haut\u00e9, A.: Fractal Geometries Theory and Applications (CRC Press, 1991)"},{"key":"3846_CR41","doi-asserted-by":"publisher","first-page":"110277","DOI":"10.1016\/j.ymssp.2023.110277","volume":"193","author":"H Sun","year":"2023","unstructured":"Sun, H., Song, L., Yu, Z.: A deep learning-based bridge damage detection and localization method. Mech. Syst. Signal Process. 193, 110277 (2023)","journal-title":"Mech. Syst. Signal Process."},{"key":"3846_CR42","doi-asserted-by":"crossref","unstructured":"Liu, T., Wang, W.: Research on intelligent recognition of bridge diseases based on yolov5 and deeplabv3+. 2023 4th International Conference on Computer Vision, Image and Deep Learning (CVIDL) 203\u2013209 (2023)","DOI":"10.1109\/CVIDL58838.2023.10166062"},{"key":"3846_CR43","doi-asserted-by":"crossref","unstructured":"Gu, J., Pan, Y., Zhang, J.: Deep learning-based intelligent detection algorithm for surface disease in concrete buildings. Buildings14 (2024). https:\/\/www.mdpi.com\/2075-5309\/14\/10\/3058","DOI":"10.3390\/buildings14103058"},{"key":"3846_CR44","doi-asserted-by":"publisher","first-page":"117708","DOI":"10.1016\/j.engstruct.2024.117708","volume":"305","author":"Z Su","year":"2024","unstructured":"Su, Z., et al.: Fractal theory based identification model for surface crack of building structures. Eng. Struct. 305, 117708 (2024)","journal-title":"Eng. Struct."},{"key":"3846_CR45","doi-asserted-by":"crossref","unstructured":"Cheng, J., Chen, Q., Huang, X.: An algorithm for crack detection, segmentation, and fractal dimension estimation in low-light environments by fusing fft and convolutional neural network. Fractal and Fractional7 (2023)","DOI":"10.3390\/fractalfract7110820"},{"key":"3846_CR46","unstructured":"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\u201311874 (2021)"},{"key":"3846_CR47","doi-asserted-by":"crossref","unstructured":"Wang, J. et\u00a0al. Carafe: Content-aware reassembly of features. Proceedings of the IEEE\/CVF international conference on computer vision 3007\u20133016 (2019)","DOI":"10.1109\/ICCV.2019.00310"},{"key":"3846_CR48","doi-asserted-by":"crossref","unstructured":"Wang, C.-Y., Bochkovskiy, A., Liao, H.-Y.\u00a0M.: Yolov7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition 7464\u20137475 (2023)","DOI":"10.1109\/CVPR52729.2023.00721"}],"container-title":["Signal, Image and Video Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-025-03846-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11760-025-03846-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-025-03846-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,13]],"date-time":"2025-02-13T14:47:18Z","timestamp":1739458038000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11760-025-03846-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,28]]},"references-count":48,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2025,3]]}},"alternative-id":["3846"],"URL":"https:\/\/doi.org\/10.1007\/s11760-025-03846-w","relation":{},"ISSN":["1863-1703","1863-1711"],"issn-type":[{"value":"1863-1703","type":"print"},{"value":"1863-1711","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,28]]},"assertion":[{"value":"24 October 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 December 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 January 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 January 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"243"}}