{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,1,31]],"date-time":"2025-01-31T15:10:07Z","timestamp":1738336207908,"version":"3.35.0"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2025,1,2]],"date-time":"2025-01-02T00:00:00Z","timestamp":1735776000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,2]],"date-time":"2025-01-02T00:00:00Z","timestamp":1735776000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62176034"],"award-info":[{"award-number":["62176034"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012669","name":"Natural Science Foundation Project of Chongqing, Chongqing Science and Technology Commission","doi-asserted-by":"publisher","award":["cstc2021jcyjmsxmX0518"],"award-info":[{"award-number":["cstc2021jcyjmsxmX0518"]}],"id":[{"id":"10.13039\/501100012669","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SIViP"],"published-print":{"date-parts":[[2025,2]]},"DOI":"10.1007\/s11760-024-03740-x","type":"journal-article","created":{"date-parts":[[2025,1,3]],"date-time":"2025-01-03T03:36:23Z","timestamp":1735875383000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A method of hierarchical feature fusion and adaptive receptive field for concrete pavement crack detection"],"prefix":"10.1007","volume":"19","author":[{"given":"Zhong","family":"Qu","sequence":"first","affiliation":[]},{"given":"Bin","family":"Yuan","sequence":"additional","affiliation":[]},{"given":"Guoqing","family":"Mu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,1,2]]},"reference":[{"issue":"13","key":"3740_CR1","doi-asserted-by":"publisher","first-page":"2686","DOI":"10.3390\/app9132686","volume":"9","author":"J Zhang","year":"2019","unstructured":"Zhang, J., Lu, C., Wang, J.: Concrete cracks detection based on fcn with dilated convolution. Appl. Sci. 9(13), 2686 (2019)","journal-title":"Appl. Sci."},{"issue":"5","key":"3740_CR2","doi-asserted-by":"publisher","first-page":"1389","DOI":"10.1049\/ipr2.12417","volume":"16","author":"Q Zhou","year":"2022","unstructured":"Zhou, Q., Qu, Z., Ju, F..r: A multi-scale learning method with dilated convolutional network for concrete surface cracks detection. IET Image Process. 16(5), 1389\u20131402 (2022)","journal-title":"IET Image Process."},{"key":"3740_CR3","doi-asserted-by":"publisher","first-page":"105808","DOI":"10.1016\/j.engappai.2022.105808","volume":"119","author":"J Zhong","year":"2023","unstructured":"Zhong, J., Huyan, J., Zhang, W.: A deeper generative adversarial network for grooved cement concrete pavement crack detection. Eng. Appl. Artif. Intell. 119, 105808 (2023)","journal-title":"Eng. Appl. Artif. Intell."},{"issue":"8","key":"3740_CR4","doi-asserted-by":"publisher","first-page":"11710","DOI":"10.1109\/TITS.2021.3106647","volume":"23","author":"Z Qu","year":"2021","unstructured":"Qu, Z., Chen, W., Wang, S.Y.: A crack detection algorithm for concrete pavement based on attention mechanism and multi-features fusion. IEEE Trans. Intell. Transp. Syst. 23(8), 11710\u201311719 (2021)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"3740_CR5","doi-asserted-by":"publisher","first-page":"131852","DOI":"10.1016\/j.conbuildmat.2023.131852","volume":"391","author":"F Guo","year":"2023","unstructured":"Guo, F., Liu, J., Lv, C.: A novel transformer-based network with attention mechanism for automatic pavement crack detection. Constr. Build. Mater. 391, 131852 (2023)","journal-title":"Constr. Build. Mater."},{"key":"3740_CR6","doi-asserted-by":"publisher","first-page":"103921","DOI":"10.1016\/j.compind.2023.103921","volume":"149","author":"Y Liu","year":"2023","unstructured":"Liu, Y., Chen, J., Hou, J.: Learning position information from attention: end-to-end weakly supervised crack segmentation with gans. Comput. Ind. 149, 103921 (2023)","journal-title":"Comput. Ind."},{"issue":"3","key":"3740_CR7","doi-asserted-by":"publisher","first-page":"1498","DOI":"10.1109\/TIP.2018.2878966","volume":"28","author":"Q Zou","year":"2018","unstructured":"Zou, Q., Zhang, Z., Li, Q.: Deepcrack: learning hierarchical convolutional features for crack detection. IEEE Trans. Image Process. 28(3), 1498\u20131512 (2018)","journal-title":"IEEE Trans. Image Process."},{"key":"3740_CR8","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1016\/j.neucom.2019.01.036","volume":"338","author":"Y Liu","year":"2019","unstructured":"Liu, Y., Yao, J., Lu, X.: Deepcrack: a deep hierarchical feature learning architecture for crack segmentation. Neurocomputing 338, 139\u2013153 (2019)","journal-title":"Neurocomputing"},{"key":"3740_CR9","first-page":"1","volume":"71","author":"M Zhang","year":"2022","unstructured":"Zhang, M., Liu, D., Wang, Q., Zhao, B., Bai, O., Sun, J.: Gait pattern recognition based on plantar pressure signals and acceleration signals. IEEE Trans. Instrum. Meas. 71, 1\u201315 (2022)","journal-title":"IEEE Trans. Instrum. Meas."},{"issue":"4","key":"3740_CR10","doi-asserted-by":"publisher","first-page":"3324","DOI":"10.1109\/TITS.2020.3035663","volume":"23","author":"Y Yu","year":"2020","unstructured":"Yu, Y., Guan, H., Li, D.: Ccapfpn: a context-augmented capsule feature pyramid network for pavement crack detection. IEEE Trans. Intell. Transp. Syst. 23(4), 3324\u20133335 (2020)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"issue":"1","key":"3740_CR11","doi-asserted-by":"publisher","first-page":"1495","DOI":"10.3233\/JIFS-191105","volume":"40","author":"Y Wu","year":"2021","unstructured":"Wu, Y., Yang, W., Pan, J.: Asphalt pavement crack detection based on multi-scale full convolutional network. J. Intell. Fuzzy Syst. 40(1), 1495\u20131508 (2021)","journal-title":"J. Intell. Fuzzy Syst."},{"issue":"9","key":"3740_CR12","doi-asserted-by":"publisher","first-page":"2115","DOI":"10.1080\/14680629.2021.1925578","volume":"23","author":"D Ma","year":"2022","unstructured":"Ma, D., Fang, H., Wang, N.: A real-time crack detection algorithm for pavement based on cnn with multiple feature layers. Road Mater. Pavement Des. 23(9), 2115\u20132131 (2022)","journal-title":"Road Mater. Pavement Des."},{"issue":"10","key":"3740_CR13","doi-asserted-by":"publisher","first-page":"18736","DOI":"10.1109\/TITS.2022.3154746","volume":"23","author":"Q Zhou","year":"2022","unstructured":"Zhou, Q., Qu, Z., Wang, S.Y.: A method of potentially promising network for crack detection with enhanced convolution and dynamic feature fusion. IEEE Trans. Intell. Transp. Syst. 23(10), 18736\u201318745 (2022)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"issue":"4","key":"3740_CR14","doi-asserted-by":"publisher","first-page":"898","DOI":"10.3390\/electronics12040898","volume":"12","author":"C Xu","year":"2023","unstructured":"Xu, C., Zhang, Q., Mei, L.: Dense multiscale feature learning transformer embedding cross-shaped attention for road damage detection. Electronics 12(4), 898 (2023)","journal-title":"Electronics"},{"key":"3740_CR15","first-page":"1","volume":"71","author":"Q Zhou","year":"2022","unstructured":"Zhou, Q., Qu, Z., Li, Y.X.: Tunnel crack detection with linear seam based on mixed attention and multiscale feature fusion. IEEE Trans. Instrum. Meas. 71, 1\u201311 (2022)","journal-title":"IEEE Trans. Instrum. Meas."},{"issue":"10","key":"3740_CR16","doi-asserted-by":"publisher","first-page":"1561","DOI":"10.3390\/buildings12101561","volume":"12","author":"H Su","year":"2022","unstructured":"Su, H., Wang, X., Han, T.: Research on a u-net bridge crack identification and feature-calculation methods based on a cbam attention mechanism. Buildings 12(10), 1561 (2022)","journal-title":"Buildings"},{"issue":"10","key":"3740_CR17","doi-asserted-by":"publisher","first-page":"18392","DOI":"10.1109\/TITS.2022.3158670","volume":"23","author":"X Sun","year":"2022","unstructured":"Sun, X., Xie, Y., Jiang, L.: Dma-net: deeplab with multi-scale attention for pavement crack segmentation. IEEE Trans. Intell. Transp. Syst. 23(10), 18392\u201318403 (2022)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"issue":"5","key":"3740_CR18","doi-asserted-by":"publisher","first-page":"3006","DOI":"10.1177\/14759217221126170","volume":"22","author":"J Hang","year":"2023","unstructured":"Hang, J., Wu, Y., Li, Y., Lai, T., Zhang, J., Li, Y.: A deep learning semantic segmentation network with attention mechanism for concrete crack detection. Struct. Health Monit. 22(5), 3006\u20133026 (2023)","journal-title":"Struct. Health Monit."},{"key":"3740_CR19","doi-asserted-by":"publisher","first-page":"104853","DOI":"10.1016\/j.autcon.2023.104853","volume":"150","author":"L Yang","year":"2023","unstructured":"Yang, L., Bai, S., Liu, Y.: Multi-scale triple-attention network for pixelwise crack segmentation. Autom. Constr. 150, 104853 (2023)","journal-title":"Autom. Constr."},{"key":"3740_CR20","first-page":"967","volume":"25","author":"J Zhong","year":"2023","unstructured":"Zhong, J., Ma, Y., Zhang, M., et al.: A pavement crack translator for data augmentation and pixel-level detection based on weakly supervised learning. IEEE Trans. Intell. Transp. Syst. 25, 967 (2023)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"issue":"4","key":"3740_CR21","doi-asserted-by":"publisher","first-page":"1525","DOI":"10.1109\/TITS.2019.2910595","volume":"21","author":"F Yang","year":"2019","unstructured":"Yang, F., Zhang, L., Yu, S.: Feature pyramid and hierarchical boosting network for pavement crack detection. IEEE Trans. Intell. Transp. Syst. 21(4), 1525\u20131535 (2019)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"3740_CR22","doi-asserted-by":"publisher","first-page":"117367","DOI":"10.1016\/j.conbuildmat.2019.117367","volume":"234","author":"Y Ren","year":"2020","unstructured":"Ren, Y., Huang, J., Hong, Z.: Image-based concrete crack detection in tunnels using deep fully convolutional networks. Constr. Build. Mater. 234, 117367 (2020)","journal-title":"Constr. Build. Mater."},{"issue":"9","key":"3740_CR23","doi-asserted-by":"publisher","first-page":"16038","DOI":"10.1109\/TITS.2022.3147669","volume":"23","author":"Z Qu","year":"2022","unstructured":"Qu, Z., Wang, C.Y., Wang, S.Y.: A method of hierarchical feature fusion and connected attention architecture for pavement crack detection. IEEE Trans. Intell. Transp. Syst. 23(9), 16038\u201316047 (2022)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"3740_CR24","doi-asserted-by":"crossref","unstructured":"Xu, S., Hao, M., Liu, G.: Concrete crack segmentation based on convolution-deconvolution feature fusion with holistically nested networks. Struct. Control Health Monit. 29(8), 2965 (2022)","DOI":"10.1002\/stc.2965"},{"key":"3740_CR25","doi-asserted-by":"publisher","first-page":"104436","DOI":"10.1016\/j.autcon.2022.104436","volume":"141","author":"J Zhong","year":"2022","unstructured":"Zhong, J., Zhu, J., Huyan, J., Ma, T., Zhang, W.: Multi-scale feature fusion network for pixel-level pavement distress detection. Autom. Constr. 141, 104436 (2022)","journal-title":"Autom. Constr."},{"key":"3740_CR26","doi-asserted-by":"crossref","unstructured":"Li, X., Wang, W., Hu, X.: Elective kernel networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 510\u2013519 (2019)","DOI":"10.1109\/CVPR.2019.00060"},{"key":"3740_CR27","doi-asserted-by":"crossref","unstructured":"Fan, R., Wang, H., Bocus, M. J.: We learn better road pothole detection: from attention aggregation to adversarial domain adaptation. In: Proceeding of European Conference Computer Vision, pp. 285\u2013300 (2020)","DOI":"10.1007\/978-3-030-66823-5_17"},{"key":"3740_CR28","doi-asserted-by":"crossref","unstructured":"Fu, J., Liu, J., Tian, H., : Dual attention network for scene segmentation. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3146\u20133154 (2019)","DOI":"10.1109\/CVPR.2019.00326"},{"issue":"12","key":"3740_CR29","doi-asserted-by":"publisher","first-page":"3434","DOI":"10.1109\/TITS.2016.2552248","volume":"17","author":"Y Shi","year":"2016","unstructured":"Shi, Y., Cui, L., Qi, Z.: Automatic road crack detection using random structured forests. IEEE Trans. Intell. Transp. Syst. 17(12), 3434\u20133445 (2016)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"3740_CR30","unstructured":"Chen, J., Lu, Y., Yu, Q., et al.: Transunet: transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306 (2021)"},{"key":"3740_CR31","doi-asserted-by":"crossref","unstructured":"Liu, H., Miao, X., Mertz, C.: Crackformer: transformer network for fine-grained crack detection. In: Proceeding IEEE\/CVF International Conference Computer Vision, pp. 3783\u20133792 (2021)","DOI":"10.1109\/ICCV48922.2021.00376"},{"issue":"11","key":"3740_CR32","doi-asserted-by":"publisher","first-page":"22135","DOI":"10.1109\/TITS.2021.3095507","volume":"23","author":"C Han","year":"2021","unstructured":"Han, C., Ma, T., Huyan, J.: Crackw-net: a novel pavement crack image segmentation convolutional neural network. IEEE Trans. Intell. Transp. Syst. 23(11), 22135\u201322144 (2021)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"3740_CR33","first-page":"1","volume":"70","author":"H Chen","year":"2021","unstructured":"Chen, H., Lin, H.: An effective hybrid atrous convolutional network for pixel-level crack detection. IEEE Trans. Instrum. Meas. 70, 1\u201312 (2021)","journal-title":"IEEE Trans. Instrum. Meas."},{"issue":"10","key":"3740_CR34","doi-asserted-by":"publisher","first-page":"18736","DOI":"10.1109\/TITS.2022.3154746","volume":"23","author":"Q Zhou","year":"2022","unstructured":"Zhou, Q., Qu, Z., Wang, S.: A method of potentially promising network for crack detection with enhanced convolution and dynamic feature fusion. IEEE Trans. Intell. Transp. Syst. 23(10), 18736\u201318745 (2022)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"issue":"6","key":"3740_CR35","doi-asserted-by":"publisher","first-page":"5981","DOI":"10.1109\/TITS.2023.3331769","volume":"25","author":"S Bai","year":"2024","unstructured":"Bai, S., Yang, L., Liu, Y.: Dmf-net: a dual-encoding multi-scale fusion network for pavement crack detection. IEEE Trans. Intell. Transp. Syst. 25(6), 5981\u20135996 (2024)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"issue":"9","key":"3740_CR36","doi-asserted-by":"publisher","first-page":"9240","DOI":"10.1109\/TITS.2023.3266776","volume":"24","author":"H Liu","year":"2023","unstructured":"Liu, H., Yang, J., Miao, X.: Crackformer network for pavement crack segmentation. IEEE Trans. Intell. Transp. Syst. 24(9), 9240\u20139252 (2023)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"issue":"11","key":"3740_CR37","doi-asserted-by":"publisher","first-page":"12686","DOI":"10.1109\/TITS.2023.3287533","volume":"24","author":"L Yang","year":"2023","unstructured":"Yang, L., Huang, H., Kong, S.: Paf-net: a progressive and adaptive fusion network for pavement crack segmentation. IEEE Trans. Intell. Transp. Syst. 24(11), 12686\u201312700 (2023)","journal-title":"IEEE Trans. Intell. Transp. Syst."}],"container-title":["Signal, Image and Video Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-024-03740-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11760-024-03740-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-024-03740-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,31]],"date-time":"2025-01-31T14:57:38Z","timestamp":1738335458000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11760-024-03740-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,2]]},"references-count":37,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2025,2]]}},"alternative-id":["3740"],"URL":"https:\/\/doi.org\/10.1007\/s11760-024-03740-x","relation":{},"ISSN":["1863-1703","1863-1711"],"issn-type":[{"type":"print","value":"1863-1703"},{"type":"electronic","value":"1863-1711"}],"subject":[],"published":{"date-parts":[[2025,1,2]]},"assertion":[{"value":"3 May 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 November 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 November 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 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 no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"177"}}