{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T19:51:41Z","timestamp":1785354701648,"version":"3.55.0"},"reference-count":45,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,2,10]],"date-time":"2025-02-10T00:00:00Z","timestamp":1739145600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,2,10]],"date-time":"2025-02-10T00:00:00Z","timestamp":1739145600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Auton. Intell. Syst."],"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>Road damage detection is an important aspect of road maintenance. Traditional manual inspections are laborious and imprecise. With the rise of deep learning technology, pavement detection methods employing deep neural networks give an efficient and accurate solution. However, due to background diversity, limited resolution, and fracture similarity, it is tough to detect road cracks with high accuracy. In this study, we offer a unique, efficient and accurate road crack damage detection, namely YOLOv8-ES. We present a novel dynamic convolutional layer(EDCM) that successfully increases the feature extraction capabilities for small fractures. At the same time, we also present a new attention mechanism (SGAM). It can effectively retain crucial information and increase the network feature extraction capacity. The Wise-IoU technique contains a dynamic, non-monotonic focusing mechanism designed to return to the goal-bounding box more precisely, especially for low-quality samples. We validate our method on both RDD2022 and VOC2007 datasets. The experimental results suggest that YOLOv8-ES performs well. This unique approach provides great support for the development of intelligent road maintenance systems and is projected to achieve further advances in future applications.<\/jats:p>","DOI":"10.1007\/s43684-025-00091-3","type":"journal-article","created":{"date-parts":[[2025,2,10]],"date-time":"2025-02-10T00:02:25Z","timestamp":1739145745000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Efficient and accurate road crack detection technology based on YOLOv8-ES"],"prefix":"10.1007","volume":"5","author":[{"given":"Kaili","family":"Zeng","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rui","family":"Fan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6038-9623","authenticated-orcid":false,"given":"Xiaoyu","family":"Tang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,2,10]]},"reference":[{"issue":"1","key":"91_CR1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s43684-023-00059-1","volume":"4","author":"J. Bchle","year":"2024","unstructured":"J. Bchle, J. Hringer, N. Khler, K.K. Zer, M. Enzweiler, R. Marchthaler, Competing with autonomous model vehicles: a software stack for driving in smart city environments. Auton. Intell. Syst. 4(1), 1\u201313 (2024)","journal-title":"Auton. Intell. Syst."},{"issue":"1","key":"91_CR2","doi-asserted-by":"publisher","DOI":"10.1007\/s43684-024-00073-x","volume":"4","author":"Q. Zhan","year":"2024","unstructured":"Q. Zhan, Y. Zhou, J. Zhang, C. Sun, R. Shen, B. He, A novel method for measuring center-axis velocity of unmanned aerial vehicles through synthetic motion blur images. Auton. Intell. Syst. 4(1), 16 (2024)","journal-title":"Auton. Intell. Syst."},{"issue":"1","key":"91_CR3","doi-asserted-by":"publisher","DOI":"10.1007\/s43684-022-00045-z","volume":"2","author":"J. Dinneweth","year":"2022","unstructured":"J. Dinneweth, A. Boubezoul, R. Mandiau, S. Espi\u00e9, Multi-agent reinforcement learning for autonomous vehicles: a survey. Auton. Intell. Syst. 2(1), 27 (2022)","journal-title":"Auton. Intell. Syst."},{"issue":"1","key":"91_CR4","doi-asserted-by":"publisher","DOI":"10.1007\/s43684-021-00015-x","volume":"1","author":"X. Wang","year":"2021","unstructured":"X. Wang, X. Qi, P. Wang, J. Yang, Decision making framework for autonomous vehicles driving behavior in complex scenarios via hierarchical state machine. Auton. Intell. Syst. 1(1), 12 (2021)","journal-title":"Auton. Intell. Syst."},{"key":"91_CR5","doi-asserted-by":"publisher","DOI":"10.1007\/s43684-022-00023-5","volume":"2","author":"W. Zhou","year":"2022","unstructured":"W. Zhou, D. Chen, J. Yan, Z. Li, H. Yin, W. Ge, Multi-agent reinforcement learning for cooperative lane changing of connected and autonomous vehicles in mixed traffic. Auton. Intell. Syst. 2, 5 (2022)","journal-title":"Auton. Intell. Syst."},{"key":"91_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.aei.2020.101182","volume":"46","author":"M.T. Cao","year":"2020","unstructured":"M.T. Cao, Q.V. Tran, N.M. Nguyen, K.T. Chang, Survey on performance of deep learning models for detecting road damages using multiple dashcam image resources. Adv. Eng. Inform. 46, 101182 (2020)","journal-title":"Adv. Eng. Inform."},{"issue":"3","key":"91_CR7","doi-asserted-by":"publisher","first-page":"4292","DOI":"10.1109\/TIV.2023.3326136","volume":"9","author":"L. Fan","year":"2024","unstructured":"L. Fan, D. Wang, J. Wang, Y. Li, Y. Cao, Y. Liu, et al., Pavement defect detection with deep learning: a comprehensive survey. IEEE Trans. Intell. Veh. 9(3), 4292\u20134311 (2024)","journal-title":"IEEE Trans. Intell. Veh."},{"key":"91_CR8","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/j.autcon.2019.03.003","volume":"103","author":"N. Wang","year":"2019","unstructured":"N. Wang, X. Zhao, P. Zhao, Y. Zhang, Z. Zou, J. Ou, Automatic damage detection of historic masonry buildings based on mobile deep learning. Autom. Constr. 103, 53\u201366 (2019)","journal-title":"Autom. Constr."},{"issue":"10","key":"91_CR9","doi-asserted-by":"publisher","first-page":"15016","DOI":"10.1109\/TITS.2024.3401754","volume":"25","author":"C. Liu","year":"2024","unstructured":"C. Liu, J. Zhao, C. Zhu, X. Xia, H. Long, MECFNet: reconstruct sharp image for UAV-based crack detection. IEEE Trans. Intell. Transp. Syst. 25(10), 15016\u201315028 (2024)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"issue":"10","key":"91_CR10","doi-asserted-by":"publisher","first-page":"3701","DOI":"10.1109\/JSTARS.2018.2865528","volume":"11","author":"Y. Pan","year":"2018","unstructured":"Y. Pan, X. Zhang, G. Cervone, L. Yang, Detection of asphalt pavement potholes and cracks based on the unmanned aerial vehicle multispectral imagery. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 11(10), 3701\u20133712 (2018)","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"issue":"2","key":"91_CR11","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1111\/mice.12701","volume":"37","author":"Z. Wang","year":"2022","unstructured":"Z. Wang, Y. Zhang, K.M. Mosalam, Y. Gao, S. Huang, Deep semantic segmentation for visual understanding on construction sites. Comput.-Aided Civ. Infrastruct. Eng. 37(2), 145\u2013162 (2022)","journal-title":"Comput.-Aided Civ. Infrastruct. Eng."},{"issue":"11","key":"91_CR12","doi-asserted-by":"publisher","first-page":"4906","DOI":"10.1109\/TITS.2019.2947206","volume":"21","author":"R. Fan","year":"2020","unstructured":"R. Fan, L.M. Road, Damage detection based on unsupervised disparity map segmentation. IEEE Trans. Intell. Transp. Syst. 21(11), 4906\u20134911 (2020)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"issue":"7","key":"91_CR13","doi-asserted-by":"publisher","first-page":"5799","DOI":"10.1109\/TCYB.2021.3060461","volume":"52","author":"R. Fan","year":"2022","unstructured":"R. Fan, U. Ozgunalp, Y. Wang, M. Liu, I. Pitas, Rethinking road surface 3-D reconstruction and pothole detection: from perspective transformation to disparity map segmentation. IEEE Trans. Cybern. 52(7), 5799\u20135808 (2022)","journal-title":"IEEE Trans. Cybern."},{"issue":"12","key":"91_CR14","doi-asserted-by":"publisher","first-page":"1090","DOI":"10.1111\/mice.12412","volume":"33","author":"X. Yang","year":"2018","unstructured":"X. Yang, H. Li, Y. Yu, X. Luo, T. Huang, X. Yang, Automatic pixel-level crack detection and measurement using fully convolutional network. Comput.-Aided Civ. Infrastruct. Eng. 33(12), 1090\u20131109 (2018)","journal-title":"Comput.-Aided Civ. Infrastruct. Eng."},{"key":"91_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2021.109316","volume":"178","author":"Y. Xu","year":"2021","unstructured":"Y. Xu, D. Li, Q. Xie, Q. Wu, J. Wang, Automatic defect detection and segmentation of tunnel surface using modified Mask R-CNN. Measurement 178, 109316 (2021)","journal-title":"Measurement"},{"issue":"3","key":"91_CR16","doi-asserted-by":"publisher","first-page":"3091","DOI":"10.1109\/TITS.2022.3221067","volume":"24","author":"C. Lin","year":"2023","unstructured":"C. Lin, D. Tian, X. Duan, J. Zhou, D. Zhao, D. Cao, DA-RDD: toward domain adaptive road damage detection across different countries. IEEE Trans. Intell. Transp. Syst. 24(3), 3091\u20133103 (2023)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"91_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.conbuildmat.2024.135025","volume":"414","author":"C. Xiong","year":"2024","unstructured":"C. Xiong, T. Zayed, E.M. Abdelkader, A novel YOLOv8-GAM-Wise-IoU model for automated detection of bridge surface cracks. Constr. Build. Mater. 414, 135025 (2024)","journal-title":"Constr. Build. Mater."},{"issue":"10","key":"91_CR18","doi-asserted-by":"publisher","first-page":"13667","DOI":"10.1109\/TITS.2024.3391751","volume":"25","author":"S. Wang","year":"2024","unstructured":"S. Wang, H. Jiao, X. Su, Q. Yuan, An ensemble learning approach with attention mechanism for detecting pavement distress and disaster-induced road damage. IEEE Trans. Intell. Transp. Syst. 25(10), 13667\u201313681 (2024)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"issue":"10","key":"91_CR19","doi-asserted-by":"publisher","first-page":"14725","DOI":"10.1109\/TITS.2024.3389945","volume":"25","author":"J. Li","year":"2024","unstructured":"J. Li, Z. Qu, S.Y. Wang, S.F. Xia, YOLOX-RDD: a method of anchor-free road damage detection for front-view images. IEEE Trans. Intell. Transp. Syst. 25(10), 14725\u201314739 (2024)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"91_CR20","doi-asserted-by":"crossref","unstructured":"C.Y. Wang, A. Bochkovskiy, H.Y.M. Liao, YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. arXiv e-prints (2022). arXiv:2207.02696","DOI":"10.1109\/CVPR52729.2023.00721"},{"issue":"1","key":"91_CR21","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s43684-021-00019-7","volume":"2","author":"M.B. Prakash","year":"2022","unstructured":"M.B. Prakash, K.C. Sriharipriya, Enhanced pothole detection system using YOLOX algorithm. Auton. Intell. Syst. 2(1), 1\u201316 (2022)","journal-title":"Auton. Intell. Syst."},{"key":"91_CR22","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1111\/mice.12692","volume":"37","author":"Y.Z. Lin","year":"2021","unstructured":"Y.Z. Lin, Z. Nie, H. Ma, Dynamics-based cross-domain structural damage detection through deep transfer learning. Comput.-Aided Civ. Infrastruct. Eng. 37, 24\u201354 (2021)","journal-title":"Comput.-Aided Civ. Infrastruct. Eng."},{"issue":"1","key":"91_CR23","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1109\/TPAMI.2015.2437384","volume":"38","author":"R. Girshick","year":"2015","unstructured":"R. Girshick, J. Donahue, T. Darrell, J. Malik, Region-based convolutional networks for accurate object detection and segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 38(1), 142\u2013158 (2015)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"6","key":"91_CR24","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","volume":"39","author":"S. Ren","year":"2017","unstructured":"S. Ren, K. He, R. Girshick, J. Sun, Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137\u20131149 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"91_CR25","volume-title":"SSD: Single Shot MultiBox Detector","author":"L. Wei","year":"2016","unstructured":"L. Wei, A. Dragomir, E. Dumitru, S. Christian, R. Scott, F. Cheng-Yang, et al., SSD: Single Shot MultiBox Detector (Springer, Cham, 2016)"},{"key":"91_CR26","volume-title":"2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","author":"M. Tan","year":"2020","unstructured":"M. Tan, R. Pang, Q.V. Le, EfficientDet: scalable and efficient object detection, in 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020)"},{"key":"91_CR27","doi-asserted-by":"publisher","first-page":"5602","DOI":"10.1109\/BigData50022.2020.9377751","volume-title":"2020 IEEE International Conference on Big Data (Big Data)","author":"S. Naddaf-Sh","year":"2020","unstructured":"S. Naddaf-Sh, M.M. Naddaf-Sh, A.R. Kashani, H. Zargarzadeh, An efficient and scalable deep learning approach for road damage detection, in 2020 IEEE International Conference on Big Data (Big Data) (IEEE, 2020), pp.\u00a05602\u20135608"},{"key":"91_CR28","volume":"73","author":"G. Ye","year":"2023","unstructured":"G. Ye, J. Qu, J. Tao, W. Dai, Y. Mao, Q. Jin, Autonomous surface crack identification of concrete structures based on the YOLOv7 algorithm. J.\u00a0Build. Eng. 73, 106688 (2023)","journal-title":"J.\u00a0Build. Eng."},{"key":"91_CR29","first-page":"7132","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","author":"J. Hu","year":"2018","unstructured":"J. Hu, L. Shen, G. Sun, Squeeze-and-excitation networks, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2018), pp.\u00a07132\u20137141"},{"key":"91_CR30","first-page":"3","volume-title":"Proceedings of the European Conference on Computer Vision (ECCV)","author":"S. Woo","year":"2018","unstructured":"S. Woo, J. Park, J.Y. Lee, K.IS. Cbam, Convolutional block attention module, in Proceedings of the European Conference on Computer Vision (ECCV) (2018), pp.\u00a03\u201319"},{"key":"91_CR31","unstructured":"P.J. Bam, Bottleneck attention module. arXiv preprint (2018). arXiv:1807.06514"},{"key":"91_CR32","first-page":"1161","volume-title":"Proceedings of the Asian Conference on Computer Vision","author":"H. Zhang","year":"2022","unstructured":"H. Zhang, K. Zu, J. Lu, Y. Zou, D. Meng EPSANet: an efficient pyramid squeeze attention block on convolutional neural network, in Proceedings of the Asian Conference on Computer Vision (2022), pp.\u00a01161\u20131177"},{"key":"91_CR33","unstructured":"Y. Liu, Z. Shao, N. Hoffmann, Global attention mechanism: Retain information to enhance channel-spatial interactions. arXiv preprint (2021). arXiv:2112.05561"},{"key":"91_CR34","unstructured":"C. Li, A. Zhou, A. Yao, Omni-dimensional dynamic convolution. arXiv preprint (2022). arXiv:2209.07947"},{"key":"91_CR35","first-page":"6070","volume-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","author":"Y. Qi","year":"2023","unstructured":"Y. Qi, Y. He, X. Qi, Y. Zhang, G. Yang, Dynamic snake convolution based on topological geometric constraints for tubular structure segmentation, in Proceedings of the IEEE\/CVF International Conference on Computer Vision (2023), pp.\u00a06070\u20136079"},{"key":"91_CR36","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","author":"J. Redmon","year":"2016","unstructured":"J. Redmon, You only look once: unified, real-time object detection, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016) arXiv:1506.02640"},{"key":"91_CR37","doi-asserted-by":"publisher","first-page":"516","DOI":"10.1145\/2964284.2967274","volume-title":"Proceedings of the 24th ACM International Conference on Multimedia","author":"J. Yu","year":"2016","unstructured":"J. Yu, Y. Jiang, Z. Wang, Z. Cao, H.T. Unitbox, An advanced object detection network, in Proceedings of the 24th ACM International Conference on Multimedia (2016), pp.\u00a0516\u2013520"},{"key":"91_CR38","unstructured":"S. Ma, Y. Xu, Mpdiou: a loss for efficient and accurate bounding box regression. arXiv preprint (2023). arXiv:2307.07662"},{"key":"91_CR39","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1016\/j.neucom.2022.07.042","volume":"506","author":"Y.F. Zhang","year":"2022","unstructured":"Y.F. Zhang, W. Ren, Z. Zhang, Z. Jia, L. Wang, T. Tan, Focal and efficient IOU loss for accurate bounding box regression. Neurocomputing 506, 146\u2013157 (2022)","journal-title":"Neurocomputing"},{"key":"91_CR40","unstructured":"Z. Tong, Y. Chen, Z. Xu, R. Yu, Wise-IoU: bounding box regression loss with dynamic focusing mechanism. arXiv preprint (2023). arXiv:2301.10051"},{"key":"91_CR41","first-page":"70","volume":"11","author":"B. Ma","year":"2024","unstructured":"B. Ma, Z. Hua, Y. Wen, H. Deng, Y. Zhao, L. Pu, et al., Using an improved lightweight YOLOv8 model for real-time detection of multi-stage apple fruit in complex orchard environments. Artif. Intell. Agric. 11, 70\u201382 (2024)","journal-title":"Artif. Intell. Agric."},{"key":"91_CR42","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2023.108304","volume":"214","author":"X. Du","year":"2023","unstructured":"X. Du, H. Cheng, Z. Ma, W. Lu, M. Wang, Z. Meng, et al., DSW-YOLO: a detection method for ground-planted strawberry fruits under different occlusion levels. Comput. Electron. Agric. 214, 108304 (2023)","journal-title":"Comput. Electron. Agric."},{"key":"91_CR43","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.123394","volume":"248","author":"H. Zheng","year":"2024","unstructured":"H. Zheng, G. Wang, D. Xiao, H. Liu, X. Hu, FTA-DETR: an efficient and precise fire detection framework based on an end-to-end architecture applicable to embedded platforms. Expert Syst. Appl. 248, 123394 (2024)","journal-title":"Expert Syst. Appl."},{"key":"91_CR44","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2020.103119","volume":"113","author":"J. Zhang","year":"2020","unstructured":"J. Zhang, X. Yang, W. Li, S. Zhang, Y. Jia, Automatic detection of moisture damages in asphalt pavements from GPR data with deep CNN and IRS method. Autom. Constr. 113, 103119 (2020)","journal-title":"Autom. Constr."},{"key":"91_CR45","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2021.103596","volume":"125","author":"F. Guo","year":"2021","unstructured":"F. Guo, Y. Qian, Y. Shi, Real-time railroad track components inspection based on the improved YOLOv4 framework. Autom. Constr. 125, 103596 (2021)","journal-title":"Autom. Constr."}],"container-title":["Autonomous Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s43684-025-00091-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s43684-025-00091-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s43684-025-00091-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,10]],"date-time":"2025-02-10T00:02:33Z","timestamp":1739145753000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s43684-025-00091-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,10]]},"references-count":45,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["91"],"URL":"https:\/\/doi.org\/10.1007\/s43684-025-00091-3","relation":{},"ISSN":["2730-616X"],"issn-type":[{"value":"2730-616X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,10]]},"assertion":[{"value":"28 September 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 December 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 January 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 February 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 confirm that they have no competing interests with any third-party organizations related to this work.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"4"}}