{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,13]],"date-time":"2025-12-13T07:18:43Z","timestamp":1765610323521,"version":"build-2065373602"},"reference-count":30,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,3,15]],"date-time":"2023-03-15T00:00:00Z","timestamp":1678838400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>As road mileage continues to expand, the number of disasters caused by expanding pavement cracks is increasing. Two main methods, image processing and deep learning, are used to detect these cracks to improve the efficiency and quality of pavement crack segmentation. The classical segmentation network, UNet, has a poor ability to extract target edge information and small target segmentation, and is susceptible to the influence of distracting objects in the environment, thus failing to better segment the tiny cracks on the pavement. To resolve this problem, we propose a U-shaped network, ALP-UNet, which adds an attention module to each encoding layer. In the decoding phase, we incorporated the Laplacian pyramid to make the feature map contain more boundary information. We also propose adding a PAN auxiliary head to provide an additional loss for the backbone to improve the overall network segmentation effect. The experimental results show that the proposed method can effectively reduce the interference of other factors on the pavement and effectively improve the mIou and mPA values compared to the previous methods.<\/jats:p>","DOI":"10.3390\/info14030182","type":"journal-article","created":{"date-parts":[[2023,3,15]],"date-time":"2023-03-15T04:39:45Z","timestamp":1678855185000},"page":"182","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Deep Learning-Based Semantic Segmentation Methods for Pavement Cracks"],"prefix":"10.3390","volume":"14","author":[{"given":"Yu","family":"Zhang","sequence":"first","affiliation":[{"name":"College of Mechanical Engineering, Shenyang University of Technology, Shenyang 110870, China"}]},{"given":"Xin","family":"Gao","sequence":"additional","affiliation":[{"name":"College of Mechanical Engineering, Shenyang University of Technology, Shenyang 110870, China"}]},{"given":"Hanzhong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Hong Kong Community College, The Hong Kong Polytechnic University, Hong Kong 999077, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e2551","DOI":"10.1002\/stc.2551","article-title":"CrackU-net: A novel deep convolutional neural network for pixelwise pavement crack detection","volume":"27","author":"Huyan","year":"2020","journal-title":"Struct. Control Health Monit."},{"key":"ref_2","unstructured":"Wang, K.C.P., and Elliott, R.P. (2016). Investigation of Image Archiving for Pavement Surface Distress Survey, Springer."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1109\/TITS.2012.2208630","article-title":"Automatic road crack detection and characterization","volume":"14","author":"Oliveira","year":"2012","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"322","DOI":"10.1016\/j.conbuildmat.2017.09.110","article-title":"Deep convolutional neural networks with transfer learning for computer vision-based data-driven pavement distress detection","volume":"157","author":"Gopalakrishnan","year":"2017","journal-title":"Constr. Build. Mater."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"04018037","DOI":"10.1061\/(ASCE)CP.1943-5487.0000781","article-title":"Image processing\u2013based classification of asphalt pavement cracks using support vector machine optimized by artificial bee colony","volume":"32","author":"Hoang","year":"2018","journal-title":"J. Comput. Civ. Eng."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1111\/mice.12263","article-title":"Deep learning-based crack damage detection using convolutional neural networks","volume":"32","author":"Cha","year":"2017","journal-title":"Comput.-Aided Civ. Infrastruct. Eng."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3434","DOI":"10.1109\/TITS.2016.2552248","article-title":"Automatic road crack detection using random structured forests","volume":"17","author":"Shi","year":"2016","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Oliveira, H., and Correia, P.L. (September, January 28). Road surface crack detection: Improved segmentation with pixel-based refinement. Proceedings of the IEEE 2017 25th European Signal Processing Conference (EUSIPCO), Kos, Greece.","DOI":"10.23919\/EUSIPCO.2017.8081565"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Lu, G., Zhao, Q., Liao, J., and He, Y. (2016, January 17\u201318). Pavement crack identification based on automatic threshold iterative method. Proceedings of the Seventh International Conference on Electronics and Information Engineering, Nanjing, China.","DOI":"10.1117\/12.2265253"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Dinh, T.H., Ha, Q.P., and La, H.M. (2016, January 13\u201315). Computer vision-based method for concrete crack detection. Proceedings of the 2016 14th international conference on control, automation, robotics and vision (ICARCV), Phuket, Thailand.","DOI":"10.1109\/ICARCV.2016.7838682"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Zhang, L., Yang, F., Zhang, Y.D., and Zhu, Y.J. (2016, January 25\u201328). Road crack detection using deep convolutional neural network. Proceedings of the 2016 IEEE International Conference on Image Processing (ICIP), Phoenix, AZ, USA.","DOI":"10.1109\/ICIP.2016.7533052"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"805","DOI":"10.1111\/mice.12297","article-title":"Automated pixel-level pavement crack detection on 3D asphalt surfaces using a deep-learning network","volume":"32","author":"Zhang","year":"2017","journal-title":"Comput.-Aided Civ. Infrastruct. Eng."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Mandal, V., Uong, L., and Adu-Gyamfi, Y. (2018, January 10\u201313). Automated road crack detection using deep convolutional neural networks. Proceedings of the 2018 IEEE International Conference on Big Data (Big Data), Seattle, WA, USA.","DOI":"10.1109\/BigData.2018.8622327"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"K\u00f6nig, J., Jenkins, M.D., Barrie, P., Mannion, M., and Morison, G. (2019, January 22\u201325). A convolutional neural network for pavement surface crack segmentation using residual connections and attention gating. Proceedings of the 2019 IEEE International Conference on Image Processing (ICIP), Taipei, Taiwan.","DOI":"10.1109\/ICIP.2019.8803060"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1016\/j.proeng.2017.02.004","article-title":"Semi-automatic inspection tool of pavement condition from three-dimensional profile scans","volume":"172","author":"Garbowski","year":"2017","journal-title":"Procedia Eng."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1316","DOI":"10.1109\/TMI.2019.2948320","article-title":"Modified U-Net (mU-Net) with incorporation of object-dependent high level features for improved liver and liver-tumor segmentation in CT images","volume":"39","author":"Seo","year":"2019","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2015: 18th International Conference, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.Y., and Kweon, I.S. (2018, January 8\u201314). Cbam: Convolutional block attention module. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Lai, W.S., Huang, J.B., Ahuja, N., and Yang, M.H. (2017, January 21\u201326). Deep laplacian pyramid networks for fast and accurate super-resolution. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.618"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201323). Squeeze-and-excitation networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_21","unstructured":"Park, J., Woo, S., Lee, J.Y., and Kweon, I.S. (2018). Bam: Bottleneck attention module. arXiv."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"4381","DOI":"10.1109\/TCSVT.2021.3049869","article-title":"Monocular depth estimation using laplacian pyramid-based depth residuals","volume":"31","author":"Song","year":"2021","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"103823","DOI":"10.1016\/j.compbiomed.2020.103823","article-title":"Multi-modal medical image fusion by Laplacian pyramid and adaptive sparse representation","volume":"123","author":"Wang","year":"2020","journal-title":"Comput. Biol. Med."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Ghiasi, G., and Fowlkes, C.C. (2016, January 8\u201316). Laplacian pyramid reconstruction and refinement for semantic segmentation. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46487-9_32"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Liu, S., Qi, L., Qin, H., Shi, J., and Jia, J. (2018, January 18\u201323). Path aggregation network for instance segmentation. Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00913"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Zhao, R., Qian, B., Zhang, X., Li, Y., Wei, R., Liu, Y., and Pan, Y. (2020, January 17\u201320). Rethinking dice loss for medical image segmentation. Proceedings of the 2020 IEEE International Conference on Data Mining (ICDM), Sorrento, Italy.","DOI":"10.1109\/ICDM50108.2020.00094"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1451","DOI":"10.1109\/TMI.2006.880587","article-title":"Generalized overlap measures for evaluation and validation in medical image analysis","volume":"25","author":"Crum","year":"2006","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1109\/TPAMI.2018.2858826","article-title":"Focal loss for dense object detection","volume":"42","author":"Lin","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_29","unstructured":"Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., and Antiga, L. (2019). Pytorch: An imperative style, high-performance deep learning library. Adv. Neural Inf. Process. Syst., 32."},{"key":"ref_30","unstructured":"Bottou, L. (2012). Neural Networks: Tricks of the Trade, Springer."}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/14\/3\/182\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:55:20Z","timestamp":1760122520000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/14\/3\/182"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,15]]},"references-count":30,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2023,3]]}},"alternative-id":["info14030182"],"URL":"https:\/\/doi.org\/10.3390\/info14030182","relation":{},"ISSN":["2078-2489"],"issn-type":[{"type":"electronic","value":"2078-2489"}],"subject":[],"published":{"date-parts":[[2023,3,15]]}}}