{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T05:15:29Z","timestamp":1780463729856,"version":"3.54.1"},"reference-count":51,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2023,8,19]],"date-time":"2023-08-19T00:00:00Z","timestamp":1692403200000},"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":["41971346"],"award-info":[{"award-number":["41971346"]}],"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":["21A420005"],"award-info":[{"award-number":["21A420005"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the Key Scientific Research Projects of Colleges and Universities in Henan Province","award":["41971346"],"award-info":[{"award-number":["41971346"]}]},{"name":"the Key Scientific Research Projects of Colleges and Universities in Henan Province","award":["21A420005"],"award-info":[{"award-number":["21A420005"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Bridge crack detection based on deep learning is a research area of great interest and difficulty in the field of bridge health detection. This study aimed to investigate the effectiveness of coupling a deep learning framework (DLF) with a convolutional neural network (CNN) for bridge crack detection. A dataset consisting of 2068 bridge crack images was randomly split into training, verification, and testing sets with a ratio of 8:1:1, respectively. Several CNN models, including Faster R-CNN, Single Shot MultiBox Detector (SSD), You Only Look Once (YOLO)-v5(x), U-Net, and Pyramid Scene Parsing Network (PSPNet), were used to conduct experiments using the PyTorch, TensorFlow2, and Keras frameworks. The experimental results show that the Harmonic Mean (F1) values of the detection results of the Faster R-CNN and SSD models under the Keras framework are relatively large (0.76 and 0.67, respectively, in the object detection model). The YOLO-v5(x) model of the TensorFlow2 framework achieved the highest F1 value of 0.67. In semantic segmentation models, the U-Net model achieved the highest detection result accuracy (AC) value of 98.37% under the PyTorch framework. The PSPNet model achieved the highest AC value of 97.86% under the TensorFlow2 framework. These experimental results provide optimal coupling efficiency parameters of a DLF and CNN for bridge crack detection. A more accurate and efficient DLF and CNN model for bridge crack detection has been obtained, which has significant practical application value.<\/jats:p>","DOI":"10.3390\/s23167272","type":"journal-article","created":{"date-parts":[[2023,8,21]],"date-time":"2023-08-21T01:46:56Z","timestamp":1692582416000},"page":"7272","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Research on the Efficiency of Bridge Crack Detection by Coupling Deep Learning Frameworks with Convolutional Neural Networks"],"prefix":"10.3390","volume":"23","author":[{"given":"Kaifeng","family":"Ma","sequence":"first","affiliation":[{"name":"College of Surveying and Geo-Informatics, North China University of Water Resources and Electric Power, Zhengzhou 450046, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiang","family":"Meng","sequence":"additional","affiliation":[{"name":"College of Surveying and Geo-Informatics, North China University of Water Resources and Electric Power, Zhengzhou 450046, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mengshu","family":"Hao","sequence":"additional","affiliation":[{"name":"College of Surveying and Geo-Informatics, North China University of Water Resources and Electric Power, Zhengzhou 450046, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guiping","family":"Huang","sequence":"additional","affiliation":[{"name":"College of Surveying and Geo-Informatics, North China University of Water Resources and Electric Power, Zhengzhou 450046, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingfeng","family":"Hu","sequence":"additional","affiliation":[{"name":"College of Surveying and Geo-Informatics, North China University of Water Resources and Electric Power, Zhengzhou 450046, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peipei","family":"He","sequence":"additional","affiliation":[{"name":"College of Surveying and Geo-Informatics, North China University of Water Resources and Electric Power, Zhengzhou 450046, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,19]]},"reference":[{"key":"ref_1","first-page":"30","article-title":"A concrete crack recognition method based on progressive cascade convolution neural network","volume":"51","author":"Lu","year":"2021","journal-title":"Ind. Constr."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Zhou, S., Pan, Y., Huang, X., Yang, D., Ding, Y., and Duan, R. (2022). Crack texture feature identification of fiber reinforced concrete based on deep learning. Materials, 15.","DOI":"10.3390\/ma15113940"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Ren, J., Zhao, G., Ma, Y., Zhao, D., Liu, T., and Yan, J. (2022). Automatic pavement crack detection fusing attention mechanism. Electronics, 11.","DOI":"10.3390\/electronics11213622"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"102946","DOI":"10.1016\/j.autcon.2019.102946","article-title":"Detection of sealed and unsealed cracks with complex backgrounds using deep convolutional neural network","volume":"107","author":"Huyan","year":"2019","journal-title":"Autom. Constr."},{"key":"ref_5","first-page":"1056","article-title":"Bridge crack classification and measurement method based on deep convolutional neural network","volume":"40","author":"Liang","year":"2020","journal-title":"Comput. Appl."},{"key":"ref_6","first-page":"2243","article-title":"Tunnel crack identification based on deep learning","volume":"43","author":"Liu","year":"2018","journal-title":"J. Guangxi Univ. Nat. Sci. Ed."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"687","DOI":"10.1080\/10798587.2010.10643111","article-title":"Acquirement and analysis of bridge crack images","volume":"16","author":"Li","year":"2010","journal-title":"Intell. Autom. Soft Comput."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"104017","DOI":"10.1016\/j.autcon.2021.104017","article-title":"Automatic damage detection using anchor-free method and unmanned surface vessel","volume":"133","author":"He","year":"2022","journal-title":"Autom. Constr."},{"key":"ref_9","first-page":"161","article-title":"Influence of cracks on corrosion initiation in bridge decks","volume":"114","author":"Balakumaran","year":"2017","journal-title":"ACI Mater. J."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.autcon.2016.08.033","article-title":"A self organizing map optimization based image recognition and processing model for bridge crack inspection","volume":"73","author":"Chen","year":"2017","journal-title":"Autom. Constr."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"123896","DOI":"10.1016\/j.conbuildmat.2021.123896","article-title":"A UAV-based machine vision method for bridge crack recognition and width quantification through hybrid feature learning","volume":"299","author":"Peng","year":"2021","journal-title":"Constr. Build. Mater."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2605","DOI":"10.1007\/s11771-013-1775-5","article-title":"Crack detection of reinforced concrete bridge using video image","volume":"20","author":"Xu","year":"2013","journal-title":"J. Cent. South Univ."},{"key":"ref_13","unstructured":"Li, L., Chan, P., Rao, A., and Lytton, R.L. (1991, January 18-21). Flexible pavement distress evaluation using image analysis. Proceedings of the Applications of Advanced Technologies in Transportation Engineering, International Conference, 2nd, Minneapolis, MN, USA."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"89","DOI":"10.3141\/2225-10","article-title":"Dynamic programming and connected component analysis for an enhanced pavement distress segmentation algorithm","volume":"2225","author":"Huang","year":"2011","journal-title":"J. Transp. Res. Board"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"700","DOI":"10.1061\/(ASCE)0733-947X(1992)118:5(700)","article-title":"Histogram-based approach for automated pavement-crack sensing","volume":"118","author":"Kirschke","year":"1992","journal-title":"J. Transp. Eng."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1061\/(ASCE)TE.1943-5436.0000051","article-title":"Critical assessment of pavement distress segmentation methods","volume":"136","author":"Tsai","year":"2010","journal-title":"J. Transp. Eng."},{"key":"ref_17","first-page":"88","article-title":"Identification of spalled concrete and exposed reinforcement in reinforced concrete bridge based on deep learning","volume":"48","author":"Ruan","year":"2020","journal-title":"World Bridges"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"105225","DOI":"10.1016\/j.engappai.2022.105225","article-title":"Automated bridge surface crack detection and segmentation using computer vision-based deep learning model","volume":"115","author":"Zhang","year":"2022","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_19","first-page":"2366","article-title":"Bridge crack detection method based on convolutional neural network","volume":"42","author":"Liao","year":"2021","journal-title":"Comput. Eng. Des."},{"key":"ref_20","first-page":"569","article-title":"Instance segmentation scheme for roofs in rural areas based on Mask R-CNN","volume":"25","author":"Sey","year":"2022","journal-title":"Egypt. J. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"5054740","DOI":"10.1155\/2020\/5054740","article-title":"An improved nondestructive semantic segmentation method for concrete dam surface crack images with high resolution","volume":"2020","author":"Zhang","year":"2020","journal-title":"Math. Probl. Eng."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Fu, H., Meng, D., Li, W., and Wang, Y. (2021). Bridge crack semantic segmentation based on improved Deeplabv3+. J. Mar. Sci. Eng., 9.","DOI":"10.3390\/jmse9060671"},{"key":"ref_23","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":"2020","journal-title":"Struct. Health Monit."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Ochoa-Ruiz, G., Angulo-Murillo, A.A., Ochoa-Zezzatti, A., Aguilar-Lobo, L.M., Vega-Fern\u00e1ndez, J.A., and Natraj, S. (2020). An asphalt damage dataset and detection system based on RetinaNet for road conditions assessment. Appl. Sci., 10.","DOI":"10.3390\/app10113974"},{"key":"ref_25","first-page":"187","article-title":"Concrete Pavement Crack Detection Algorithm Based on Full U-net","volume":"48","author":"Qu","year":"2021","journal-title":"Comput. Sci."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Jia, J., Fu, M., Liu, X., and Zheng, B. (2022). Underwater object detection based on improved efficientDet. Remote Sens., 14.","DOI":"10.3390\/rs14184487"},{"key":"ref_27","first-page":"4261","article-title":"An algorithm for target detection of engineering vehicles based on improved centerNet","volume":"73","author":"Yu","year":"2022","journal-title":"Comput. Mater. Contin."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"104698","DOI":"10.1016\/j.autcon.2022.104698","article-title":"Automatic recognition of pavement cracks from combined GPR B-scan and C-scan images using multiscale feature fusion deep neural networks","volume":"146","author":"Liu","year":"2023","journal-title":"Autom. Constr."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Wang, D., Liu, Z., Gu, X., Wu, W., Chen, Y., and Wang, L. (2022). Automatic detection of pothole distress in asphalt pavement using improved convolutional neural networks. Remote Sens., 14.","DOI":"10.3390\/rs14163892"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1639","DOI":"10.1007\/s40747-022-00876-6","article-title":"Automated bridge crack detection method based on lightweight vision models","volume":"9","author":"Zhang","year":"2022","journal-title":"Complex Intell. Syst."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"03015","DOI":"10.1051\/shsconf\/202214403015","article-title":"YOLO V5s-based deep learning approach for concrete cracks detection","volume":"144","author":"Yu","year":"2022","journal-title":"SHS Web Conf."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"6654996","DOI":"10.1155\/2021\/6654996","article-title":"A crack identification method for concrete structures using improved U-Net convolutional neural networks","volume":"2021","author":"Qiao","year":"2021","journal-title":"Math. Probl. Eng."},{"key":"ref_33","unstructured":"Li, L. (2019). Deep Learning Theory and Practice (Fundamentals), Electronic Industry Press."},{"key":"ref_34","first-page":"91","article-title":"Faster r-cnn: Towards real-time object detection with region proposal networks","volume":"28","author":"Ren","year":"2015","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"129659","DOI":"10.1016\/j.conbuildmat.2022.129659","article-title":"Automatic bridge crack detection using unmanned aerial vehicle and Faster R-CNN","volume":"362","author":"Li","year":"2023","journal-title":"Constr. Build. Mater."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., and Berg, A.C. (2016, January 11\u201314). SSD: Single Shot Multibox Detector. Proceedings of the Computer Vision-ECCV\u2013ECCV 2016, 14th European Conference, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"150925","DOI":"10.1109\/ACCESS.2021.3125703","article-title":"Automated asphalt highway pavement crack detection based on deformable single shot multi-box detector under a complex environment","volume":"9","author":"Yan","year":"2021","journal-title":"IEEE Access"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Inam, H., Ul, N., and Usman, M. (2023). Smart and automated infrastructure management: A deep learning approach for crack detection in bridge images. Sustainability, 15.","DOI":"10.3390\/su15031866"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Yu, G., and Zhou, X. (2023). An improved YOLOv5 crack detection method combined with a bottleneck transformer. Mathematics, 11.","DOI":"10.3390\/math11102377"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Su, H., Wang, X., Han, T., Wang, Z., Zhao, Z., and Zhang, P. (2022). Research on a U-Net bridge crack identification and feature-calculation methods based on a CBAM attention mechanism. Buildings, 12.","DOI":"10.3390\/buildings12101561"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"04023004","DOI":"10.1061\/JITSE4.ISENG-2218","article-title":"Automated bridge crack detection based on improving encoder\u2013decoder network and strip pooling","volume":"29","author":"Li","year":"2023","journal-title":"J. Infrastruct. Syst."},{"key":"ref_42","first-page":"1727","article-title":"Research on bridge crack detection algorithm based on deep learning","volume":"45","author":"Li","year":"2019","journal-title":"J. Autom."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1761","DOI":"10.1109\/LGRS.2019.2909541","article-title":"Improved faster R-CNN with multiscale feature fusion and homography augmentation for vehicle detection in remote sensing images","volume":"16","author":"Ji","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"114602","DOI":"10.1016\/j.eswa.2021.114602","article-title":"A survey and performance evaluation of deep learning methods for small object detection","volume":"172","author":"Liu","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"00368504221128487","DOI":"10.1177\/00368504221128487","article-title":"Crack detection for concrete bridges with imaged based deep learning","volume":"105","author":"Wan","year":"2022","journal-title":"Sci. Prog."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1016\/j.precisioneng.2020.10.008","article-title":"Evaluation of subsurface damage layer of BK7 glass via cross-sectional surface nanoindentation","volume":"67","author":"Wang","year":"2021","journal-title":"Precis. Eng."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1813821","DOI":"10.1155\/2022\/1813821","article-title":"Intelligent crack detection and quantification in the concrete bridge: A deep learning-assisted image processing approach","volume":"2022","author":"Yu","year":"2022","journal-title":"Adv. Civ. Eng."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"121949","DOI":"10.1016\/j.conbuildmat.2020.121949","article-title":"Detection of concealed cracks from ground penetrating radar images based on deep learning algorithm","volume":"273","author":"Li","year":"2021","journal-title":"Constr. Build. Mater."},{"key":"ref_49","first-page":"35","article-title":"Method for bridge crack detection based on the U-Net convolutional networks","volume":"46","author":"Zhu","year":"2019","journal-title":"J. Xi\u2019an Univ. Electron. Sci. Technol."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1080\/22797254.2021.2018944","article-title":"Building extraction from remote sensing images using deep residual U-Net","volume":"55","author":"Wang","year":"2022","journal-title":"Eur. J. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"4453","DOI":"10.3233\/JIFS-201296","article-title":"Intelligent crack damage detection system in shield tunnel using combination of retinanet and optimal adaptive selection","volume":"40","author":"Gao","year":"2021","journal-title":"J. Intell. Fuzzy Syst."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/16\/7272\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:37:30Z","timestamp":1760128650000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/16\/7272"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,19]]},"references-count":51,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2023,8]]}},"alternative-id":["s23167272"],"URL":"https:\/\/doi.org\/10.3390\/s23167272","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,19]]}}}