{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T14:17:23Z","timestamp":1740147443044,"version":"3.37.3"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,12,2]],"date-time":"2024-12-02T00:00:00Z","timestamp":1733097600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,2]],"date-time":"2024-12-02T00:00:00Z","timestamp":1733097600000},"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"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SIViP"],"published-print":{"date-parts":[[2025,1]]},"DOI":"10.1007\/s11760-024-03581-8","type":"journal-article","created":{"date-parts":[[2024,12,2]],"date-time":"2024-12-02T16:40:22Z","timestamp":1733157622000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A crack detection network with multi-channel attention and enhanced information interaction"],"prefix":"10.1007","volume":"19","author":[{"given":"Zhong","family":"Qu","sequence":"first","affiliation":[]},{"given":"Lihui","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Xuehui","family":"Yin","sequence":"additional","affiliation":[]},{"given":"Tong","family":"Lu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,12,2]]},"reference":[{"key":"3581_CR1","doi-asserted-by":"crossref","unstructured":"Krishnamoorthy, S., Zhang, C., Yanxin, Z.: Implementation of image fusion to investigate wall crack. In: International Conference on Emerging Trends in Information Technology and Engineering, pp. 1\u20133, Vellore, India (February 2020)","DOI":"10.1109\/ic-ETITE47903.2020.397"},{"key":"3581_CR2","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., Xiaohu, L., Xie, R., Li, L.: Deepcrack: a deep hierarchical feature learning architecture for crack segmentation. Neurocomputing 338, 139\u2013153 (2019)","journal-title":"Neurocomputing"},{"issue":"1","key":"3581_CR3","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1109\/TITS.2012.2208630","volume":"14","author":"H Oliveira","year":"2012","unstructured":"Oliveira, H., Correia, P.L.: Automatic road crack detection and characterization. IEEE Trans. Intell. Transp. Syst. 14(1), 155\u2013168 (2012)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"3581_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11760-024-03197-y","volume":"18","author":"J He","year":"2024","unstructured":"He, J., Wang, Y., Wang, Y., Li, R., Zhang, D., Zheng, Z.: A lightweight road crack detection algorithm based on improved yolov7 model. Signal Image Video Process. 18, 1\u201314 (2024)","journal-title":"Signal Image Video Process."},{"issue":"2","key":"3581_CR5","doi-asserted-by":"publisher","first-page":"1929","DOI":"10.1007\/s11760-023-02905-4","volume":"18","author":"S Sheng","year":"2024","unstructured":"Sheng, S., Yin, H., Yang, Y., Chong, A., Huang, H.: DUNet: dense u-blocks network for fine-grained crack detection. SIViP 18(2), 1929\u20131938 (2024)","journal-title":"SIViP"},{"key":"3581_CR6","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention\u2014MICCAI 2015: 18th International Conference, pp. 234\u2013241 (2015)","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"3581_CR7","volume":"131","author":"Y Xie","year":"2024","unstructured":"Xie, Y., Zhan, N., Zhu, J., Bingli, X., Chen, H., Mao, W., Luo, X., Ya, H.: Landslide extraction from aerial imagery considering context association characteristics. Int. J. Appl. Earth Obs. Geoinf. 131, 103950 (2024)","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"issue":"1","key":"3581_CR8","doi-asserted-by":"publisher","first-page":"2346259","DOI":"10.1080\/17538947.2024.2346259","volume":"17","author":"J Zhu","year":"2024","unstructured":"Zhu, J., Zhang, J., Chen, H., Xie, Y., Hengchao, G., Lian, H.: A cross-view intelligent person search method based on multi-feature constraints. Int. J. Digit. Earth 17(1), 2346259 (2024)","journal-title":"Int. J. Digit. Earth"},{"key":"3581_CR9","first-page":"1","volume":"1","author":"S Cao","year":"2024","unstructured":"Cao, S., Feng, D., Liu, S., Xu, W., Chen, H., Xie, Y., Zhang, H., Pirasteh, S., Zhu, J.: BEMRF-net: boundary enhancement and multiscale refinement fusion for building extraction from remote sensing imagery. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 1, 1\u201317 (2024)","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"issue":"12","key":"3581_CR10","doi-asserted-by":"publisher","first-page":"15105","DOI":"10.1109\/TITS.2023.3300312","volume":"24","author":"T Zhang","year":"2023","unstructured":"Zhang, T., Wang, D., Yang, L.: ECSNET: an accelerated real-time image segmentation CNN architecture for pavement crack detection. IEEE Trans. Intell. Transp. Syst. 24(12), 15105\u201315112 (2023)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"3581_CR11","first-page":"1","volume":"72","author":"G Enhui","year":"2023","unstructured":"Enhui, G., Xiao, G., Lian, F., Tongyao, M., Hong, J., Liu, J.: Segmentation and evaluation of crack image from aircraft fuel tank via atrous spatial pyramid fusion and hybrid attention network. IEEE Trans. Instrum. Meas. 72, 1\u201314 (2023)","journal-title":"IEEE Trans. Instrum. Meas."},{"issue":"3","key":"3581_CR12","doi-asserted-by":"publisher","first-page":"2296","DOI":"10.1109\/TIV.2022.3210299","volume":"8","author":"Q Zhou","year":"2023","unstructured":"Zhou, Q., Zhong, Q., Fang-rong, J.: A lightweight network for crack detection with split exchange convolution and multi-scale features fusion. IEEE Trans. Intell. Veh. 8(3), 2296\u20132306 (2023)","journal-title":"IEEE Trans. Intell. Veh."},{"issue":"11","key":"3581_CR13","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., Liu, Y., Hongnian, Yu.: 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."},{"issue":"10","key":"3581_CR14","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., Cao, Yu., Liu, B.: 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":"11","key":"3581_CR15","doi-asserted-by":"publisher","first-page":"22179","DOI":"10.1109\/TITS.2022.3177210","volume":"23","author":"H Yao","year":"2022","unstructured":"Yao, H., Liu, Y., Li, X., You, Z., Feng, Yu., Weiwei, L.: A detection method for pavement cracks combining object detection and attention mechanism. IEEE Trans. Intell. Transp. Syst. 23(11), 22179\u201322789 (2022)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"3581_CR16","doi-asserted-by":"crossref","unstructured":"Zhang, X., Huang, H.: Lightaunet: a lightweight fusing attention based U-Net for crack detection. In: 2022 7th International Conference on Image, Vision and Computing, pp. 178\u2013182, Xi\u2019an, China (2022)","DOI":"10.1109\/ICIVC55077.2022.9886163"},{"issue":"10","key":"3581_CR17","doi-asserted-by":"publisher","first-page":"18736","DOI":"10.1109\/TITS.2022.3154746","volume":"23","author":"Q Zhou","year":"2022","unstructured":"Zhou, Q., Zhong, Q., Wang, S.-Y., Bao, K.-H.: 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."},{"key":"3581_CR18","doi-asserted-by":"crossref","unstructured":"Jia, Y., Rong, C., Wu, C., Yang, Y.: Research on the decomposition and fusion method for the infrared and visible images based on the guided image filtering and gaussian filter. In: 2017 3rd IEEE International Conference on Computer and Communications, pp. 1797\u20131802, Chengdu, China (March 2017)","DOI":"10.1109\/CompComm.2017.8322849"},{"key":"3581_CR19","doi-asserted-by":"crossref","unstructured":"Huang, Zilong, Wang, Xinggang, Huang, Lichao, Huang, Chang, Wei, Yunchao, Liu, Wenyu: Ccnet: Criss-cross attention for semantic segmentation. In: Proceedings of the IEEE\/CVF international conference on computer vision, pages 603\u2013612, Long Beach, CA, USA (February 2019)","DOI":"10.1109\/ICCV.2019.00069"},{"key":"3581_CR20","doi-asserted-by":"crossref","unstructured":"Ding, X., Guo, Y., Ding, G., Han, J.: strengthening the kernel skeletons for powerful CNN via asymmetric convolution blocks. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 1911\u20131920, Long Beach, CA, USA (February 2019)","DOI":"10.1109\/ICCV.2019.00200"},{"key":"3581_CR21","doi-asserted-by":"crossref","unstructured":"Li, J., Wen, Y., He, L.: SCConv: spatial and channel reconstruction convolution for feature redundancy. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 6153\u20136162, Vancouver, Canada (August 2023)","DOI":"10.1109\/CVPR52729.2023.00596"},{"key":"3581_CR22","doi-asserted-by":"crossref","unstructured":"Zhang, Q.-L. Yang, Y.-B.: SA-Net: shuffle attention for deep convolutional neural networks. In: ICASSP 2021\u20132021 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 2235\u20132239, Toronto, Ontario, Canada (May 2021)","DOI":"10.1109\/ICASSP39728.2021.9414568"},{"issue":"12","key":"3581_CR23","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., Meng, F., Chen, 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."},{"issue":"4","key":"3581_CR24","doi-asserted-by":"publisher","first-page":"1525","DOI":"10.1109\/TITS.2019.2910595","volume":"21","author":"F Yang","year":"2020","unstructured":"Yang, F., Zhang, L., Sijia, Yu., Prokhorov, D., Mei, X., Ling, H.: Feature pyramid and hierarchical boosting network for pavement crack detection. IEEE Trans. Intell. Transp. Syst. 21(4), 1525\u20131535 (2020)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"issue":"11","key":"3581_CR25","doi-asserted-by":"publisher","first-page":"22135","DOI":"10.1109\/TITS.2021.3095507","volume":"23","author":"C Han","year":"2022","unstructured":"Han, C., Tao Ma, J., Huyan, X.H., Zhang, Y.: CrackW-Net: a novel pavement crack image segmentation convolutional neural network. IEEE Trans. Intell. Transp. Syst. 23(11), 22135\u201322144 (2022)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"3581_CR26","doi-asserted-by":"crossref","unstructured":"Ran, R., Xu, X., Qiu, S., Cui, X., Wu, F.: Crack-SegNet: surface crack detection in complex background using encoder\u2013decoder architecture. In: Proceedings of the 2021 4th International Conference on Sensors, Signal and Image Processing, pp. 15\u201322, New York, USA (October 2021)","DOI":"10.1145\/3502814.3502817"},{"key":"3581_CR27","doi-asserted-by":"crossref","unstructured":"Zou, Q., Zhang, Z., Li, Q., Qi, X., Wang, Q., Wang, S.: Deepcrack: learning hierarchical convolutional features for crack detection. IEEE Trans. Image Process. 28(3), 1498\u20131512 (2019)","DOI":"10.1109\/TIP.2018.2878966"},{"key":"3581_CR28","doi-asserted-by":"crossref","unstructured":"Liu, H., Miao, X., Mertz, C., Xu, C., Kong, H.: Crackformer: transformer network for fine-grained crack detection. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 3763\u20133772, Montreal, QC, Canada (October 2021)","DOI":"10.1109\/ICCV48922.2021.00376"},{"key":"3581_CR29","doi-asserted-by":"crossref","unstructured":"Xie, S., Tu, Z.: Holistically-nested edge detection. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 1395\u20131403, Santiago, Chile (October 2015)","DOI":"10.1109\/ICCV.2015.164"},{"issue":"12","key":"3581_CR30","doi-asserted-by":"publisher","first-page":"25489","DOI":"10.1109\/TITS.2021.3098355","volume":"23","author":"G Gao","year":"2022","unstructured":"Gao, G., Guoan, X., Yi, Yu., Xie, J., Yang, J., Yue, D.: MSCFNet: a lightweight network with multi-scale context fusion for real-time semantic segmentation. IEEE Trans. Intell. Transp. Syst. 23(12), 25489\u201325499 (2022)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"issue":"13","key":"3581_CR31","doi-asserted-by":"publisher","first-page":"2960","DOI":"10.3390\/ma13132960","volume":"13","author":"Z Fan","year":"2020","unstructured":"Fan, Z., Li, C., Chen, Y., Wei, J., Loprencipe, G., Chen, X., Di Mascio, P.: Automatic crack detection on road pavements using encoder\u2013decoder architecture. Materials 13(13), 2960 (2020)","journal-title":"Materials"},{"issue":"11","key":"3581_CR32","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., Liu, Y., Hongnian, Yu.: PAF-net: a progressive and adaptive fusion network for pavement crack segmentation. IEEE Trans. Intell. Transp. Syst. 24(11), 12686\u201318700 (2023)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"3581_CR33","unstructured":"Qi, Y., He, Y., Qi, X., Zhang, Y., Yang, G.: Dynamic snake convolution based on topological geometric constraints for tubular structure segmentation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 6070\u20136079"},{"key":"3581_CR34","doi-asserted-by":"publisher","first-page":"10763","DOI":"10.1109\/TPAMI.2024.3449959","volume":"46","author":"L Chen","year":"2024","unstructured":"Chen, L., Ying, F., Lin, G., Yan, C., Harada, T., Huang, G.: Frequency-aware feature fusion for dense image prediction. IEEE Trans. Pattern Anal. Mach. Intell. 46, 10763\u201310780 (2024)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"3581_CR35","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2023.3334266","author":"Wenwen Zheng","year":"2024","unstructured":"Zheng, Wenwen, Jiang, Xiaoyan, Fang, Zhijun, Gao, Yongbin: TV-Net: a structure-level feature fusion network based on tensor voting for road crack segmentation. IEEE Trans. Intell. Transp. Syst. (2024). https:\/\/doi.org\/10.1109\/TITS.2023.3334266","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\/s11760-024-03581-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11760-024-03581-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-024-03581-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,28]],"date-time":"2025-01-28T17:52:02Z","timestamp":1738086722000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11760-024-03581-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,2]]},"references-count":35,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["3581"],"URL":"https:\/\/doi.org\/10.1007\/s11760-024-03581-8","relation":{},"ISSN":["1863-1703","1863-1711"],"issn-type":[{"type":"print","value":"1863-1703"},{"type":"electronic","value":"1863-1711"}],"subject":[],"published":{"date-parts":[[2024,12,2]]},"assertion":[{"value":"25 September 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 November 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 November 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 December 2024","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":"37"}}