{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T17:10:56Z","timestamp":1772644256367,"version":"3.50.1"},"reference-count":60,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2023,3,10]],"date-time":"2023-03-10T00:00:00Z","timestamp":1678406400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52278177"],"award-info":[{"award-number":["52278177"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2021RC4025"],"award-info":[{"award-number":["2021RC4025"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Science and Technology Innovation Leader Project of Hunan Province","award":["52278177"],"award-info":[{"award-number":["52278177"]}]},{"name":"Science and Technology Innovation Leader Project of Hunan Province","award":["2021RC4025"],"award-info":[{"award-number":["2021RC4025"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In the present study, an integrated framework for automatic detection, segmentation, and measurement of road surface cracks is proposed. First, road images are captured, and crack regions are detected based on the fifth version of the You Only Look Once (YOLOv5) algorithm; then, a modified Residual Unity Networking (Res-UNet) algorithm is proposed for accurate segmentation at the pixel level within the crack regions; finally, a novel crack surface feature quantification algorithm is developed to determine the pixels of crack in width and length, respectively. In addition, a road crack dataset containing complex environmental noise is produced. Different shooting distances, angles, and lighting conditions are considered. Validated through the same dataset and compared with You Only Look at CoefficienTs ++ (YOLACT++) and DeepLabv3+, the proposed method shows higher accuracy for crack segmentation under complex backgrounds. Specifically, the crack damage detection based on the YOLOv5 method achieves a mean average precision of 91%; the modified Res-UNet achieves 87% intersection over union (IoU) when segmenting crack pixels, 6.7% higher than the original Res-UNet; and the developed crack surface feature algorithm has an accuracy of 95% in identifying the crack length and a root mean square error of 2.1 pixels in identifying the crack width, with the accuracy being 3% higher in length measurement than that of the traditional method.<\/jats:p>","DOI":"10.3390\/rs15061530","type":"journal-article","created":{"date-parts":[[2023,3,13]],"date-time":"2023-03-13T03:03:57Z","timestamp":1678676637000},"page":"1530","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["An Integrated Method for Road Crack Segmentation and Surface Feature Quantification under Complex Backgrounds"],"prefix":"10.3390","volume":"15","author":[{"given":"Lu","family":"Deng","sequence":"first","affiliation":[{"name":"College of Civil Engineering, Hunan University, Changsha 410082, China"},{"name":"Key Laboratory for Damage Diagnosis of Engineering Structures of Hunan Province, Hunan University, Changsha 410082, China"}]},{"given":"An","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Civil Engineering, Hunan University, Changsha 410082, China"}]},{"given":"Jingjing","family":"Guo","sequence":"additional","affiliation":[{"name":"College of Civil Engineering, Hunan University, Changsha 410082, China"},{"name":"Key Laboratory for Damage Diagnosis of Engineering Structures of Hunan Province, Hunan University, Changsha 410082, China"}]},{"given":"Yingkai","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Civil Engineering, Hunan University, Changsha 410082, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,10]]},"reference":[{"key":"ref_1","unstructured":"National Bureau of Statistics (2022, January 01). National Data, Available online: https:\/\/data.stats.gov.cn\/."},{"key":"ref_2","unstructured":"The State Council (2022, May 11). Policy Analyzing, Available online: http:\/\/www.gov.cn\/zhengce\/2022-05\/11\/content_5689580.htm."},{"key":"ref_3","unstructured":"Ministry of Transport and Logistic Services (2022, September 15). Road Maintenance, Available online: https:\/\/mot.gov.sa\/en\/Roads\/Pages\/RoadsMaintenance.aspx."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"115016","DOI":"10.1088\/0964-1726\/22\/11\/115016","article-title":"Using Piezoelectric Sensors for Ultrasonic Pulse Velocity Measurements in Concrete","volume":"22","author":"Kee","year":"2013","journal-title":"Smart Mater. Struct."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1302","DOI":"10.1016\/j.conbuildmat.2013.06.082","article-title":"Inspection, Evaluation and Repair Monitoring of Cracked Concrete Floor Using NDT Methods","volume":"48","author":"Zoidis","year":"2013","journal-title":"Constr. Build. Mater."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"8832","DOI":"10.3390\/s150408832","article-title":"Damage Detection with Streamlined Structural Health Monitoring Data","volume":"15","author":"Li","year":"2015","journal-title":"Sensors"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Dery, L., and Jelnov, A. (2021). Privacy\u2013Accuracy Consideration in Devices that Collect Sensor-Based Information. Sensors, 21.","DOI":"10.3390\/s21144684"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Jiang, S., Zhang, J., Wang, W., and Wang, Y. (2023). Automatic Inspection of Bridge Bolts Using Unmanned Aerial Vision and Adaptive Scale Unification-Based Deep Learning. Remote Sens., 15.","DOI":"10.3390\/rs15020328"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Fiorentini, N., Maboudi, M., Leandri, P., Losa, M., and Gerke, M. (2020). Surface Motion Prediction and Mapping for Road Infrastructures Management by PS-Insar Measurements and Machine Learning Algorithms. Remote Sens., 12.","DOI":"10.3390\/rs12233976"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zhu, Y., and Tang, H. (2023). Automatic Damage Detection and Diagnosis for Hydraulic Structures Using Drones and Artificial Intelligence Techniques. Remote Sens., 15.","DOI":"10.3390\/rs15030615"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1016\/j.optlaseng.2019.06.011","article-title":"High-Accuracy Multi-Camera Reconstruction Enhanced by Adaptive Point Cloud Correction Algorithm","volume":"122","author":"Chen","year":"2019","journal-title":"Opt. Lasers Eng."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Al Duhayyim, M., Malibari, A.A., Alharbi, A., Afef, K., Yafoz, A., Alsini, R., Alghushairy, O., and Mohsen, H. (2022). Road Damage Detection Using the Hunger Games Search with Elman Neural Network on High-Resolution Remote Sensing Images. Remote Sens., 14.","DOI":"10.3390\/rs14246222"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Lee, T., Yoon, Y., Chun, C., and Ryu, S. (2021). CNN-Based Road-Surface Crack Detection Model that Responds to Brightness Changes. Electronics, 10.","DOI":"10.3390\/electronics10121402"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"104436","DOI":"10.1016\/j.autcon.2022.104436","article-title":"Multi-Scale Feature Fusion Network for Pixel-Level Pavement Distress Detection","volume":"141","author":"Zhong","year":"2022","journal-title":"Autom. Constr."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1621","DOI":"10.1007\/s40996-021-00671-2","article-title":"Concrete Road Crack Detection Using Deep Learning-Based Faster R-Cnn Method","volume":"46","year":"2022","journal-title":"Iran. J. Sci. Technol. Trans. Civ. Eng."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"115406","DOI":"10.1016\/j.engstruct.2022.115406","article-title":"Automatic Classification of Asphalt Pavement Cracks Using A Novel Integrated Generative Adversarial Networks and Improved Vgg Model","volume":"277","author":"Que","year":"2023","journal-title":"Eng. Struct."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1659","DOI":"10.1080\/10298436.2020.1714047","article-title":"Pavement Distress Detection and Classification Based on YOLO Network","volume":"22","author":"Du","year":"2021","journal-title":"Int. J. Pavement Eng."},{"key":"ref_18","unstructured":"Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., Ke, Z., Li, Q., Cheng, M., and Nie, W. (2022). Yolov6: A Single-Stage Object Detection Framework for Industrial Applications. arXiv."},{"key":"ref_19","unstructured":"Wang, C.Y., Bochkovskiy, A., and Liao, H.Y.M. (2022). YOLOv7: Trainable Bag-of-Freebies Sets New State-of-the-Art for Real-Time Object Detectors. arXiv."},{"key":"ref_20","unstructured":"Ultralytics (2023, January 12). Yolov8. Available online: https:\/\/github.com\/ultralytics\/ultralytics."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Liu, C., Sui, H., Wang, J., Ni, Z., and Ge, L. (2022). Real-Time Ground-Level Building Damage Detection Based on Lightweight and Accurate Yolov5 Using Terrestrial Images. Remote Sens., 14.","DOI":"10.3390\/rs14122763"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Shokri, P., Shahbazi, M., and Nielsen, J. (2022). Semantic Segmentation and 3d Reconstruction of Concrete Cracks. Remote Sens., 14.","DOI":"10.3390\/rs14225793"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"An, Q., Chen, X., Wang, H., Yang, H., Yang, Y., Huang, W., and Wang, L. (2022). Segmentation of Concrete Cracks by Using Fractal Dimension and Uhk-Net. Fractal Fract., 6.","DOI":"10.3390\/fractalfract6020095"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Fan, J., Zhang, M., Shi, Z., Liu, R., and Guo, B. (2022). A Recurrent Adaptive Network: Balanced Learning for Road Crack Segmentation with High-Resolution Images. Remote Sens., 14.","DOI":"10.3390\/rs14143275"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Yong, P., and Wang, N. (2022). RIIAnet: A Real-Time Segmentation Network Integrated with Multi-Type Features of Different Depths for Pavement Cracks. Appl. Sci., 12.","DOI":"10.3390\/app12147066"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"18392","DOI":"10.1109\/TITS.2022.3158670","article-title":"DMA-Net: Deeplab with Multi-Scale Attention for Pavement Crack Segmentation","volume":"23","author":"Sun","year":"2022","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Shen, Y., Yu, Z., Li, C., Zhao, C., and Sun, Z. (2023). Automated Detection for Concrete Surface Cracks Based on Deeplabv3+ BDF. Buildings, 13.","DOI":"10.3390\/buildings13010118"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"103176","DOI":"10.1016\/j.autcon.2020.103176","article-title":"An Integrated Approach to Automatic Pixel-Level Crack Detection and Quantification of Asphalt Pavement","volume":"114","author":"Ji","year":"2020","journal-title":"Autom. Constr."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1109\/LGRS.2018.2802944","article-title":"Road Extraction by Deep Residual U-Net","volume":"15","author":"Zhang","year":"2018","journal-title":"IEEE Geosci. Sens. Lett."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"874","DOI":"10.1016\/j.autcon.2011.03.004","article-title":"Visual Retrieval of Concrete Crack Properties for Automated Post-Earthquake Structural Safety Evaluation","volume":"20","author":"Zhu","year":"2011","journal-title":"Autom. Constr."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"103291","DOI":"10.1016\/j.autcon.2020.103291","article-title":"Hybrid Pixel-Level Concrete Crack Segmentation and Quantification Across Complex Backgrounds Using Deep Learning","volume":"118","author":"Kang","year":"2020","journal-title":"Autom. Constr."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"115158","DOI":"10.1016\/j.engstruct.2022.115158","article-title":"Novel Visual Crack Width Measurement Based on Backbone Double-Scale Features for Improved Detection Automation","volume":"274","author":"Tang","year":"2023","journal-title":"Eng. Struct."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1145\/357994.358023","article-title":"A Fast Parallel Algorithm for Thinning Digital Patterns","volume":"27","author":"Zhang","year":"1984","journal-title":"Commun. ACM"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Guizilini, V., Li, J., Ambru\u0219, R., and Gaidon, A. (2021, January 11\u201317). Geometric Unsupervised Domain Adaptation for Semantic Segmentation. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, BC, Canada.","DOI":"10.1109\/ICCV48922.2021.00842"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Toldo, M., Michieli, U., and Zanuttigh, P. (2021, January 3\u20137). Unsupervised Domain Adaptation in Semantic Segmentation via Orthogonal and Clustered Embeddings. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, Waikola, HI, USA.","DOI":"10.1109\/WACV48630.2021.00140"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Stan, S., and Rostami, M. (2021, January 2\u20139). Unsupervised Model Adaptation for Continual Semantic Segmentation. Proceedings of the AAAI Conference on Artificial Intelligence, online.","DOI":"10.1609\/aaai.v35i3.16362"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Marsden, R.A., Wiewel, F., D\u00f6bler, M., Yang, Y., and Yang, B. (2022, January 18\u201323). Continual Unsupervised Domain Adaptation for Semantic Segmentation Using A Class-Specific Transfer. Proceedings of the 2022 International Joint Conference on Neural Networks (IJCNN), Padua, Italy.","DOI":"10.1109\/IJCNN55064.2022.9892200"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"103991","DOI":"10.1016\/j.autcon.2021.103991","article-title":"Pavement Distress Detection Using Convolutional Neural Networks with Images Captured via UAV","volume":"133","author":"Zhu","year":"2022","journal-title":"Autom. Constr."},{"key":"ref_39","unstructured":"Ruiqiang, X. (2022). YOLOv5s-GTB: Light-Weighted and Improved Yolov5s for Bridge Crack Detection. arXiv."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Jing, Y., Ren, Y., Liu, Y., Wang, D., and Yu, L. (2022). Automatic Extraction of Damaged Houses by Earthquake Based on Improved YOLOv5: A case study in Yangbi. Remote Sens., 14.","DOI":"10.3390\/rs14020382"},{"key":"ref_41","unstructured":"Ultralytics (2022, January 17). Yolov5. Available online: https:\/\/github.com\/ultralytics\/yolov5."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 21\u201326). Feature Pyramid Networks for Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_43","unstructured":"Li, H., Xiong, P., An, J., and Wang, L. (2018). Pyramid Attention Network for Semantic Segmentation. arXiv."},{"key":"ref_44","unstructured":"Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., and Kainz, B. (2018). Attention U-Net: Learning Where to Look for the Pancreas. arXiv."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Hou, H., Lan, C., Xu, Q., Lv, L., Xiong, X., Yao, F., and Wang, L. (2023). Attention-Based Matching Approach for Heterogeneous Remote Sensing Images. Remote Sens., 15.","DOI":"10.3390\/rs15010163"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"462","DOI":"10.1006\/cgip.1994.1042","article-title":"Building Skeleton Models via 3-D Medial Surface Axis Thinning Algorithms","volume":"56","author":"Lee","year":"1994","journal-title":"Graph. Models Image Process."},{"key":"ref_47","unstructured":"(2021, November 01). Home\u2014OpenCV. Available online: https:\/\/opencv.org."},{"key":"ref_48","unstructured":"(2021, June 15). Pytorch. Available online: https:\/\/pytorch.org\/."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"107133","DOI":"10.1016\/j.dib.2021.107133","article-title":"RDD2020: An Annotated Image Dataset for Automatic Road Damage Detection Using Deep Learning","volume":"36","author":"Arya","year":"2021","journal-title":"Data Brief"},{"key":"ref_50","unstructured":"(2023, January 25). Road-Crack-Images-Test. Available online: https:\/\/www.kaggle.com\/datasets\/andada\/road-crack-imagestest."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Eisenbach, M., Stricker, R., Seichter, D., Amende, K., Debes, K., Sesselmann, M., Ebersbach, D., Stoeckert, U., and Gross, H.-M. (2017, January 14\u201319). How to Get Pavement Distress Detection Ready for Deep Learning? A Systematic Approach. Proceedings of the 2017 International Joint Conference on Neural Networks (IJCNN), Anchorage, AK, USA.","DOI":"10.1109\/IJCNN.2017.7966101"},{"key":"ref_52","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_53","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1016\/j.patrec.2011.11.004","article-title":"Cracktree: Automatic Crack Detection from Pavement Images","volume":"33","author":"Zou","year":"2012","journal-title":"Pattern Recognit. Lett."},{"key":"ref_54","unstructured":"Feiyu (2022, March 23). Vimble 3. Available online: https:\/\/www.feiyu-tech.cn\/vimble-3\/."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1108","DOI":"10.1109\/TPAMI.2020.3014297","article-title":"YOLACT++: Better Real-Time Instance Segmentation","volume":"44","author":"Bolya","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Chen, L.C., Zhu, Y., and Papandreou, G. (2018, January 8\u201314). Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation. Proceedings of the European Conference on Computer Vision, Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"ref_57","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015). Medical Image Computing and Computer-Assisted Intervention\u2014MICCAI 2015, Springer."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1864","DOI":"10.1177\/1475921720940068","article-title":"A Research on An Improved Unet-Based Concrete Crack Detection Algorithm","volume":"20","author":"Zhang","year":"2021","journal-title":"Struct. Health Monit."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"126265","DOI":"10.1016\/j.conbuildmat.2021.126265","article-title":"Unet-Based Model for Crack Detection Integrating Visual Explanations","volume":"322","author":"Liu","year":"2022","journal-title":"Constr. Build. Mater."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"04016057","DOI":"10.1061\/(ASCE)CP.1943-5487.0000623","article-title":"Automated Detection of Multiple Pavement Defects","volume":"31","author":"Radopoulou","year":"2017","journal-title":"J. Comput. Civ. Eng."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/6\/1530\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:52:07Z","timestamp":1760122327000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/6\/1530"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,10]]},"references-count":60,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2023,3]]}},"alternative-id":["rs15061530"],"URL":"https:\/\/doi.org\/10.3390\/rs15061530","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,10]]}}}