{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T16:32:32Z","timestamp":1787157152986,"version":"build-2736575974"},"reference-count":56,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T00:00:00Z","timestamp":1785283200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T00:00:00Z","timestamp":1785283200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Open Research Project of Anhui Province Key Laboratory of Intelligent Building & Building Energy Saving of Anhui Jianzhu University","award":["IBES2025KF04ZD"],"award-info":[{"award-number":["IBES2025KF04ZD"]}]},{"name":"Intelligent Structures and Materials Innovation Team","award":["2025AHGXZK10023"],"award-info":[{"award-number":["2025AHGXZK10023"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Real-Time Image Proc"],"published-print":{"date-parts":[[2026,8]]},"DOI":"10.1007\/s11554-026-01944-7","type":"journal-article","created":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T13:36:52Z","timestamp":1785332212000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["MDeepLab: a real-time lightweight network for accurate road crack segmentation"],"prefix":"10.1007","volume":"23","author":[{"given":"Guangling","family":"Sun","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dongdong","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanqiu","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zuocai","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,29]]},"reference":[{"key":"1944_CR1","doi-asserted-by":"crossref","unstructured":"Bello, I., Zoph, B., Vaswani, A., Shlens, J., Le, Q.V.: Attention augmented convolutional networks. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 3286\u20133295. (2019)","DOI":"10.1109\/ICCV.2019.00338"},{"issue":"2","key":"1944_CR2","doi-asserted-by":"publisher","first-page":"793","DOI":"10.1177\/03611981251367708","volume":"2680","author":"L Chen","year":"2026","unstructured":"Chen, L., Zheng, J., Chen, Q., Jiang, L., Ngo, T.: Semantic segmentation model for road cracks based on parallel flatten swin-vanillanet framework. Transp. Res. Rec. 2680(2), 793\u2013813 (2026)","journal-title":"Transp. Res. Rec."},{"key":"1944_CR3","doi-asserted-by":"crossref","unstructured":"Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 801\u2013818. (2018)","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"1944_CR4","doi-asserted-by":"publisher","unstructured":"Chollet, F.: Xception: deep learning with depthwise separable convolutions. In: 2017 IEEE conference on computer vision and pattern recognition (CVPR), pp. 1800\u20131807 (2017). https:\/\/doi.org\/10.1109\/CVPR.2017.195","DOI":"10.1109\/CVPR.2017.195"},{"issue":"1","key":"1944_CR5","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1007\/s11554-025-01803-x","volume":"23","author":"L Dang","year":"2026","unstructured":"Dang, L., Zhang, G., Mu, H., Xie, Y.: Emdanet: real-time road crack detection in uav remote sensing images via multi-scale dual-attention network. J. Real-Time Image Proc. 23(1), 15 (2026)","journal-title":"J. Real-Time Image Proc."},{"issue":"16","key":"1944_CR6","doi-asserted-by":"publisher","first-page":"1347","DOI":"10.1007\/s11760-025-04931-w","volume":"19","author":"F Deng","year":"2025","unstructured":"Deng, F., Ren, J., Yang, S.: Bimambanet: an efficient crack segmentation method that combines attention mechanism with mamba: F. deng et al. Signal Image Video Process. 19(16), 1347 (2025)","journal-title":"Signal Image Video Process."},{"key":"1944_CR7","doi-asserted-by":"crossref","unstructured":"Eisenbach, M., Stricker, R., Seichter, D., Amende, K., Debes, K., Sesselmann, M., Ebersbach, D., Stoeckert, U., Gross, H.M.: How to get pavement distress detection ready for deep learning? a systematic approach. In: 2017 International Joint Conference on Neural Networks (IJCNN), pp. 2039\u20132047. IEEE (2017)","DOI":"10.1109\/IJCNN.2017.7966101"},{"key":"1944_CR8","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1016\/j.ijtst.2023.11.005","volume":"13","author":"H Gong","year":"2024","unstructured":"Gong, H., Liu, L., Liang, H., Zhou, Y., Cong, L.: A state-of-the-art survey of deep learning models for automated pavement crack segmentation. Int. J. Transp. Sci. Technol. 13, 44\u201357 (2024)","journal-title":"Int. J. Transp. Sci. Technol."},{"issue":"1","key":"1944_CR9","doi-asserted-by":"publisher","first-page":"69","DOI":"10.3233\/THC-220031","volume":"31","author":"S He","year":"2023","unstructured":"He, S., Xiao, B., Wei, H., Huang, S., Chen, T.: Svm classifier of cervical histopathology images based on texture and morphological features. Technol. Health Care 31(1), 69\u201380 (2023)","journal-title":"Technol. Health Care"},{"issue":"1","key":"1944_CR10","doi-asserted-by":"publisher","first-page":"4212","DOI":"10.1038\/s41467-025-59516-5","volume":"16","author":"Y Hu","year":"2025","unstructured":"Hu, Y., Chen, N., Hou, Y., Lin, X., Jing, B., Liu, P.: Lightweight deep learning for real-time road distress detection on mobile devices. Nat. Commun. 16(1), 4212 (2025)","journal-title":"Nat. Commun."},{"issue":"15","key":"1944_CR11","doi-asserted-by":"publisher","first-page":"6549","DOI":"10.3390\/app14156549","volume":"14","author":"LL Huang","year":"2024","unstructured":"Huang, L.L., Lin, J.D., Huang, W.H., Kuo, C.H., Huang, M.Y.: Application of automated pavement inspection technology in provincial highway pavement maintenance decision-making. Appl. Sci. 14(15), 6549 (2024)","journal-title":"Appl. Sci."},{"issue":"11","key":"1944_CR12","doi-asserted-by":"publisher","first-page":"2765","DOI":"10.3390\/diagnostics12112765","volume":"12","author":"SY Huang","year":"2022","unstructured":"Huang, S.Y., Hsu, W.L., Hsu, R.J., Liu, D.W.: Fully convolutional network for the semantic segmentation of medical images: a survey. Diagnostics 12(11), 2765 (2022)","journal-title":"Diagnostics"},{"issue":"3","key":"1944_CR13","first-page":"341","volume":"41","author":"YJ Jiang","year":"2021","unstructured":"Jiang, Y.J., Zhang, X.P.: Research on automatic detection and health assessment of tunnel lining. Tunnel Constr. 41(3), 341\u2013348 (2021)","journal-title":"Tunnel Constr."},{"key":"1944_CR14","doi-asserted-by":"crossref","unstructured":"Jiang, Z., Li, D., Zhao, T., Gao, M.: Efficient detection of highway pavement cracks using computer vision-based msfnet model. Case Studies in Construction Materials p. e05049 (2025)","DOI":"10.1016\/j.cscm.2025.e05049"},{"key":"1944_CR15","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1016\/j.neucom.2022.06.083","volume":"503","author":"J Jing","year":"2022","unstructured":"Jing, J., Liu, S., Wang, G., Zhang, W., Sun, C.: Recent advances on image edge detection: a comprehensive review. Neurocomputing 503, 259\u2013271 (2022)","journal-title":"Neurocomputing"},{"issue":"1","key":"1944_CR16","doi-asserted-by":"publisher","first-page":"04014110","DOI":"10.1061\/(ASCE)CP.1943-5487.0000447","volume":"1","author":"M Kamaliardakani","year":"2016","unstructured":"Kamaliardakani, M., Sun, L., Ardakani, M.K.: Sealed-crack detection algorithm using heuristic thresholding approach. J. Comput. Civ. Eng. 1(1), 04014110 (2016)","journal-title":"J. Comput. Civ. Eng."},{"key":"1944_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.conbuildmat.2021.126162","volume":"321","author":"N Kheradmandi","year":"2022","unstructured":"Kheradmandi, N., Mehranfar, V.: A critical review and comparative study on image segmentation-based techniques for pavement crack detection. Constr. Build. Mater. 321, 126162 (2022)","journal-title":"Constr. Build. Mater."},{"issue":"1","key":"1944_CR18","doi-asserted-by":"publisher","first-page":"1375","DOI":"10.1007\/s11042-021-11435-5","volume":"81","author":"J Li","year":"2022","unstructured":"Li, J., Han, Y., Zhang, M., Li, G., Zhang, B.: Multi-scale residual network model combined with global average pooling for action recognition. Multimedia Tools and Applications 81(1), 1375\u20131393 (2022)","journal-title":"Multimedia Tools and Applications"},{"issue":"10","key":"1944_CR19","doi-asserted-by":"publisher","first-page":"13833","DOI":"10.1109\/TITS.2024.3405995","volume":"25","author":"P Li","year":"2024","unstructured":"Li, P., Wang, M., Fan, Z., Huang, H., Zhu, G., Zhuang, J.: Our-net: a multi-frequency network with octave max unpooling and octave convolution residual block for pavement crack segmentation. IEEE Trans. Intell. Transp. Syst. 25(10), 13833\u201313848 (2024)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"issue":"5","key":"1944_CR20","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1007\/s11554-025-01766-z","volume":"22","author":"S Li","year":"2025","unstructured":"Li, S., Lin, Z., Shi, Y., Lan, J., Zhuo, Y.: Road crack detection algorithm based on fusion structure re-parameterization with multi-scale parallel convolutions and hybrid attention mechanism. J. Real-Time Image Proc. 22(5), 189 (2025)","journal-title":"J. Real-Time Image Proc."},{"key":"1944_CR21","doi-asserted-by":"crossref","unstructured":"Lin, B., Zhao, G., Qiu, S., Wang, X., Yan, F., Wang, C., Liu, C.: Crackawarenet: a continuity-aware lightweight network for fine crack segmentation. In: Structures, vol.\u00a088, p. 111811. Elsevier (2026)","DOI":"10.1016\/j.istruc.2026.111811"},{"key":"1944_CR22","doi-asserted-by":"crossref","unstructured":"Lin, G., Milan, A., Shen, C., Reid, I.: Refinenet: Multi-path refinement networks for high-resolution semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1925\u20131934. (2017)","DOI":"10.1109\/CVPR.2017.549"},{"issue":"3","key":"1944_CR23","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6501\/ae2cbb","volume":"37","author":"Z Lin","year":"2026","unstructured":"Lin, Z., Pan, W.: Yolo-roc: a high-precision and ultra-lightweight model for real-time road damage detection. Meas. Sci. Technol. 37(3), 035405 (2026)","journal-title":"Meas. Sci. Technol."},{"key":"1944_CR24","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.undsp.2023.09.012","volume":"17","author":"F Liu","year":"2024","unstructured":"Liu, F., Ding, W., Qiao, Y., Wang, L.: Transfer learning-based encoder-decoder model with visual explanations for infrastructure crack segmentation: New open database and comprehensive evaluation. Undergr. Space 17, 60\u201381 (2024)","journal-title":"Undergr. Space"},{"key":"1944_CR25","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431\u20133440. (2015)","DOI":"10.1109\/CVPR.2015.7298965"},{"issue":"13","key":"1944_CR26","doi-asserted-by":"publisher","first-page":"11093","DOI":"10.1109\/JIOT.2023.3239944","volume":"10","author":"S Lu","year":"2023","unstructured":"Lu, S., Lu, J., An, K., Wang, X., He, Q.: Edge computing on iot for machine signal processing and fault diagnosis: A review. IEEE Internet Things J. 10(13), 11093\u201311116 (2023)","journal-title":"IEEE Internet Things J."},{"issue":"4","key":"1944_CR27","doi-asserted-by":"publisher","first-page":"943","DOI":"10.1007\/s42947-022-00172-z","volume":"16","author":"SD Nguyen","year":"2023","unstructured":"Nguyen, S.D., Tran, T.S., Tran, V.P., Lee, H.J., Piran, M.J., Le, V.P.: Deep learning-based crack detection: a survey: Sd nguyen et al. Int. J. Pavement Res. Technol. 16(4), 943\u2013967 (2023)","journal-title":"Int. J. Pavement Res. Technol."},{"issue":"3","key":"1944_CR28","doi-asserted-by":"publisher","first-page":"3448","DOI":"10.1109\/TITS.2022.3228042","volume":"24","author":"H Pan","year":"2022","unstructured":"Pan, H., Hong, Y., Sun, W., Jia, Y.: Deep dual-resolution networks for real-time and accurate semantic segmentation of traffic scenes. IEEE Trans. Intell. Transp. Syst. 24(3), 3448\u20133460 (2022)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"issue":"2","key":"1944_CR29","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1007\/s11554-025-01634-w","volume":"22","author":"W Pan","year":"2025","unstructured":"Pan, W., Wang, X., Huan, W.: Real-time dynamic scale-aware fusion detection network: take road damage detection as an example. J. Real-Time Image Proc. 22(2), 55 (2025)","journal-title":"J. Real-Time Image Proc."},{"issue":"29","key":"1944_CR30","doi-asserted-by":"publisher","first-page":"35469","DOI":"10.1007\/s11042-025-20729-x","volume":"84","author":"V Pandey","year":"2025","unstructured":"Pandey, V., Mishra, S.S.: A review of image-based deep learning methods for crack detection. Multimed. Tools Appl. 84(29), 35469\u201335511 (2025)","journal-title":"Multimed. Tools Appl."},{"key":"1944_CR31","unstructured":"Papagiannakis, A.T., Masad, E.A.: Pavement design and materials. John Wiley & Sons (2024)"},{"key":"1944_CR32","unstructured":"Poudel, R.P., Liwicki, S., Cipolla, R.: Fast-scnn: fast semantic segmentation network. In: British Machine Vision Conference (BMVC) (2019)"},{"issue":"18","key":"1944_CR33","doi-asserted-by":"publisher","first-page":"1449","DOI":"10.1007\/s11760-025-05046-y","volume":"19","author":"J Qiao","year":"2025","unstructured":"Qiao, J., Xu, N.: An edge-enhanced u-shaped network with cross-spatial feature fusion and implicit attention upsampling for pavement crack segmentation. Signal Image Video Process. 19(18), 1449 (2025)","journal-title":"Signal Image Video Process."},{"issue":"1","key":"1944_CR34","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1007\/s11760-024-03581-8","volume":"19","author":"Z Qu","year":"2025","unstructured":"Qu, Z., Zhou, L., Yin, X., Lu, T.: A crack detection network with multi-channel attention and enhanced information interaction. SIViP 19(1), 37 (2025)","journal-title":"SIViP"},{"key":"1944_CR35","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 234\u2013241. Springer (2015)","DOI":"10.1007\/978-3-319-24574-4_28"},{"issue":"3","key":"1944_CR36","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vision 115(3), 211\u2013252 (2015)","journal-title":"Int. J. Comput. Vision"},{"key":"1944_CR37","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: Mobilenetv 2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Rrecognition, pp. 4510\u20134520. (2018)","DOI":"10.1109\/CVPR.2018.00474"},{"issue":"18","key":"1944_CR38","doi-asserted-by":"publisher","first-page":"8972","DOI":"10.3390\/app12188972","volume":"12","author":"M Shafiq","year":"2022","unstructured":"Shafiq, M., Gu, Z.: Deep residual learning for image recognition: a survey. Appl. Sci. 12(18), 8972 (2022)","journal-title":"Appl. Sci."},{"issue":"12","key":"1944_CR39","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":"1944_CR40","first-page":"04024025","volume":"40","author":"M Uhm","year":"2024","unstructured":"Uhm, M., Shin, G., Kim, H., Jeong, H.D., Kim, H.: Exploring the long-term impact of preventive road treatments on municipal highways. J. Manag. Eng. 40(4), 04024025 (2024)","journal-title":"J. Manag. Eng."},{"key":"1944_CR41","doi-asserted-by":"crossref","unstructured":"Wang, Q., Wang, M., Sun, J., Chen, D., Shi, P.: Review of surface-defect detection methods for industrial products based on machine vision. IEEE Access (2025)","DOI":"10.1109\/ACCESS.2025.3571297"},{"issue":"14","key":"1944_CR42","doi-asserted-by":"publisher","first-page":"1237","DOI":"10.1007\/s11760-025-04836-8","volume":"19","author":"X Wang","year":"2025","unstructured":"Wang, X., Liang, Y., Yang, X., Guo, M., Jia, T., Kamran, M.: A real-time multiscale pavement crack-detection model: X. wang et al. Signal Image Video Process. 19(14), 1237 (2025)","journal-title":"Signal Image Video Process."},{"key":"1944_CR43","doi-asserted-by":"publisher","DOI":"10.1016\/j.optlastec.2025.112641","volume":"186","author":"X Wang","year":"2025","unstructured":"Wang, X., Ye, F., Sun, Z., Malihi, S., Iida, F.: Road surface condition monitoring with an optical fibre reservoir structure based on acceleration data from vehicles. Opt. Laser Technol. 186, 112641 (2025)","journal-title":"Opt. Laser Technol."},{"key":"1944_CR44","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1016\/j.patrec.2024.11.025","volume":"187","author":"L Wen","year":"2025","unstructured":"Wen, L., Ye, Y., Zuo, L.: Gaf-net: a new automated segmentation method based on multiscale feature fusion and feedback module. Pattern Recogn. Lett. 187, 86\u201392 (2025)","journal-title":"Pattern Recogn. Lett."},{"key":"1944_CR45","doi-asserted-by":"publisher","first-page":"460","DOI":"10.1016\/j.neunet.2022.10.034","volume":"157","author":"X Xiao","year":"2023","unstructured":"Xiao, X., Zhao, Y., Zhang, F., Luo, B., Yu, L., Chen, B., Yang, C.: Baseg: boundary aware semantic segmentation for autonomous driving. Neural Netw. 157, 460\u2013470 (2023)","journal-title":"Neural Netw."},{"issue":"11","key":"1944_CR46","doi-asserted-by":"publisher","first-page":"5185","DOI":"10.3390\/app16115185","volume":"16","author":"Y Xie","year":"2026","unstructured":"Xie, Y., Wang, Y.: Crack segmentation model for low-quality crack images based on feature integration and triple attention. Appl. Sci. 16(11), 5185 (2026)","journal-title":"Appl. Sci."},{"key":"1944_CR47","doi-asserted-by":"crossref","unstructured":"Xu, J., Xiong, Z., Huang, S.: Pidnet: A real-time semantic segmentation network inspired by pid controllers. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11568\u201311577. (2023)","DOI":"10.1109\/CVPR52729.2023.01871"},{"key":"1944_CR48","doi-asserted-by":"crossref","unstructured":"Xu, Y., Liang, W., Qi, G., Li, Y., Wang, P., Liu, Q., Xu, H., Zhu, Z.: Drcnet: A dual-stream multi-scale information retention network for crack segmentation. Expert Systems with Applications p. 132575 (2026)","DOI":"10.1016\/j.eswa.2026.132575"},{"issue":"4","key":"1944_CR49","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., 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 (2019)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"issue":"11","key":"1944_CR50","doi-asserted-by":"publisher","first-page":"3051","DOI":"10.1007\/s11263-021-01515-2","volume":"129","author":"C Yu","year":"2021","unstructured":"Yu, C., Gao, C., Wang, J., Yu, G., Shen, C., Sang, N.: Bisenet v2: bilateral network with guided aggregation for real-time semantic segmentation. Int. J. Comput. Vision 129(11), 3051\u20133068 (2021)","journal-title":"Int. J. Comput. Vision"},{"key":"1944_CR51","unstructured":"Zhang, H., Jia, C., Liu, H., Cheng, X., Shi, F., Chen, S.: Scrwkv: ultra-compact structure-calibrated vision-rwkv for topological crack segmentation, (2026). arXiv preprint arXiv:2605.14926"},{"issue":"1","key":"1944_CR52","doi-asserted-by":"publisher","first-page":"30376","DOI":"10.1038\/s41598-024-81119-1","volume":"14","author":"J Zhang","year":"2024","unstructured":"Zhang, J., Sun, S., Song, W., Li, Y., Teng, Q.: A novel convolutional neural network for enhancing the continuity of pavement crack detection. Sci. Rep. 14(1), 30376 (2024)","journal-title":"Sci. Rep."},{"issue":"5","key":"1944_CR53","doi-asserted-by":"publisher","first-page":"168","DOI":"10.1007\/s11554-025-01747-2","volume":"22","author":"Y Zhang","year":"2025","unstructured":"Zhang, Y., Wang, T., Wang, X.: Lightweight real-time road defect detection algorithm integrating multi-coordinate aggregation attention and shared convolution. J. Real-Time Image Proc. 22(5), 168 (2025)","journal-title":"J. Real-Time Image Proc."},{"issue":"12","key":"1944_CR54","doi-asserted-by":"publisher","first-page":"1743","DOI":"10.1111\/mice.13103","volume":"39","author":"G Zhu","year":"2024","unstructured":"Zhu, G., Liu, J., Fan, Z., Yuan, D., Ma, P., Wang, M., Sheng, W., Wang, K.C.: A lightweight encoder-decoder network for automatic pavement crack detection. Comput. Aided Civ. Infrastruct. Eng. 39(12), 1743\u20131765 (2024)","journal-title":"Comput. Aided Civ. Infrastruct. Eng."},{"key":"1944_CR55","doi-asserted-by":"publisher","DOI":"10.1016\/j.aei.2025.103610","volume":"68","author":"G Zhu","year":"2025","unstructured":"Zhu, G., Shen, S.L., Yao, J., Wang, M., Zhuang, J., Fan, Z.: Automatic lightweight networks for real-time road crack detection with dpso. Adv. Eng. Inform. 68, 103610 (2025)","journal-title":"Adv. Eng. Inform."},{"key":"1944_CR56","doi-asserted-by":"crossref","unstructured":"Zim, A.H., Iqbal, A., Al-Huda, Z., Malik, A., Kuribayashi, M.: Efficientcracknet: A lightweight model for crack segmentation. In: 2025 IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV), pp. 6279\u20136289. IEEE (2025)","DOI":"10.1109\/WACV61041.2025.00612"}],"container-title":["Journal of Real-Time Image Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11554-026-01944-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11554-026-01944-7","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11554-026-01944-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T16:17:19Z","timestamp":1787156239000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11554-026-01944-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,29]]},"references-count":56,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2026,8]]}},"alternative-id":["1944"],"URL":"https:\/\/doi.org\/10.1007\/s11554-026-01944-7","relation":{},"ISSN":["1861-8200","1861-8219"],"issn-type":[{"value":"1861-8200","type":"print"},{"value":"1861-8219","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,29]]},"assertion":[{"value":"1 April 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 July 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 July 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no conflict of interest.","order":1,"name":"Ethics","label":"Conflict of interest","group":{"name":"EthicsHeading","label":"Declarations"}}],"article-number":"147"}}