{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:53:04Z","timestamp":1777704784575,"version":"3.51.4"},"reference-count":38,"publisher":"SAGE Publications","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2021,1,4]]},"abstract":"<jats:p>Pavement crack assessment is an important indicator for evaluating road health. However, due to the dark color of the asphalt pavement and the texture characteristics of the pavement, current asphalt pavement crack detection technology cannot meet the requirements of accuracy and efficiency. In this paper, we propose an end-to-end multi-scale full convolutional neural network to achieve the semantic segmentation of cracks in road images by learning the crack characteristics in the complex fine grain background of asphalt pavement. The method uses DenseNet and deconvolution network framework to achieve pixel-level detection and fuses features learned from different scales of convolutional kernels through a full convolutional network to obtain richer information on multi-scale features, allowing more detailed representation of crack features in high-resolution images. And the back end joins the SVM classifier to achieve crack classification after crack segmentation. Then we create a road test standard data set containing 12 cracks and evaluate it on the data. The experimental results show that the method achieves good segmentation effect for 12 types of cracks, and the crack segmentation for asphalt pavement is better than the most advanced methods.<\/jats:p>","DOI":"10.3233\/jifs-191105","type":"journal-article","created":{"date-parts":[[2020,10,13]],"date-time":"2020-10-13T12:49:53Z","timestamp":1602593393000},"page":"1495-1508","source":"Crossref","is-referenced-by-count":15,"title":["Asphalt pavement crack detection based on multi-scale full convolutional network"],"prefix":"10.1177","volume":"40","author":[{"given":"Yangxu","family":"Wu","sequence":"first","affiliation":[{"name":"Shanxi Key Laboratory of Signal Capturing & Processing, North University of China, Taiyuan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wanting","family":"Yang","sequence":"additional","affiliation":[{"name":"Information and Computer Science, Taiyuan University of Technology, Taiyuan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinxiao","family":"Pan","sequence":"additional","affiliation":[{"name":"Shanxi Key Laboratory of Signal Capturing & Processing, North University of China, Taiyuan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ping","family":"Chen","sequence":"additional","affiliation":[{"name":"Shanxi Key Laboratory of Signal Capturing & Processing, North University of China, Taiyuan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"key":"10.3233\/JIFS-191105_ref2","doi-asserted-by":"crossref","first-page":"e1400","DOI":"10.1002\/tal.1400","article-title":"A novel machine learning-based algorithm to detect damage in high-rise building structures","volume":"10","author":"Rafiei","year":"2017","journal-title":"Structural Design of Tall & Special Buildings"},{"key":"10.3233\/JIFS-191105_ref3","first-page":"0","article-title":"The Design of Glass Crack Detection System Based on Image Preprocessing Technology","author":"Yiyang","journal-title":"Information Technology & Artificial Intelligence Conference"},{"key":"10.3233\/JIFS-191105_ref4","doi-asserted-by":"crossref","unstructured":"Thermographic test for the geometric characterization of cracks in welding using IR image rectification, Automation in Construction 61 (2016), 58\u201365.","DOI":"10.1016\/j.autcon.2015.10.012"},{"issue":"8","key":"10.3233\/JIFS-191105_ref5","doi-asserted-by":"crossref","first-page":"1111","DOI":"10.1007\/s11340-011-9567-z","article-title":"A Fully Non-Contact Ultrasonic Propagation Imaging System for Closed Surface Crack Evaluation","volume":"52","author":"Dhital","year":"2012","journal-title":"Experimental Mechanics"},{"issue":"2","key":"10.3233\/JIFS-191105_ref6","doi-asserted-by":"crossref","first-page":"250","DOI":"10.1016\/j.nrjag.2013.12.002","article-title":"Automatic concrete cracks detection and mapping of terrestrial laser scan data","volume":"2","author":"Rabah","year":"2013","journal-title":"NRIAG Journal of Astronomy and Geophysics"},{"key":"10.3233\/JIFS-191105_ref7","doi-asserted-by":"crossref","unstructured":"Zhang Z. , et al., SemiContour: A Semi-Supervised Learning Approach for Contour Detection. (2016).","DOI":"10.1109\/CVPR.2016.34"},{"key":"10.3233\/JIFS-191105_ref8","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1177\/0361198105194000112","article-title":"Development of a Crack Type Index Transportation Research Record","volume":"1940","author":"Lee","year":"2005","journal-title":"Journal of the Transportation Research Board"},{"key":"10.3233\/JIFS-191105_ref9","doi-asserted-by":"crossref","unstructured":"Ayenu-Prah A. and Attoh-Okine N. , Evaluating Pavement Cracks with Bidimensional Empirical Mode Decomposition, EURASIP Journal on Advances in Signal Processing, Article ID 861701, (2008).","DOI":"10.1155\/2008\/861701"},{"key":"10.3233\/JIFS-191105_ref10","first-page":"0","article-title":"The Design of Glass Crack Detection System Based on Image Preprocessing Technology","author":"Yiyang","journal-title":"Information Technology & Artificial Intelligence Conference"},{"issue":"4","key":"10.3233\/JIFS-191105_ref11","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.autcon.2013.06.011","article-title":"Image-based retrieval of concrete crack properties for bridge inspection","volume":"39","author":"Adhikari","year":"2014","journal-title":"Automation in Construction"},{"key":"10.3233\/JIFS-191105_ref12","doi-asserted-by":"crossref","first-page":"73","DOI":"10.3141\/2024-09","article-title":"Wavelet-Based Pavement Distress Image Edge Detection with a Trous Algorithm Transporta- \u2018tion Research Record","volume":"2024","author":"Wang","year":"2008","journal-title":"Journal of the Transportation Research Board"},{"issue":"1","key":"10.3233\/JIFS-191105_ref13","doi-asserted-by":"crossref","first-page":"013017","DOI":"10.1117\/1.2177650","article-title":"Automatic inspection of pavement cracking distress","volume":"15","author":"Huang","year":"2006","journal-title":"Journal of Electronic Imaging"},{"issue":"10","key":"10.3233\/JIFS-191105_ref14","doi-asserted-by":"crossref","first-page":"759","DOI":"10.1111\/mice.12141","article-title":"Vision-Based Automated Crack Detection for Bridge Inspection","volume":"30","author":"Yeum","year":"2015","journal-title":"Computer-Aided Civil and Infrastructure Engineering"},{"issue":"11","key":"10.3233\/JIFS-191105_ref15","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-Based Learning Applied to Document Recognition","volume":"86","author":"Lecun","year":"1998","journal-title":"Proceedings of the IEEE"},{"issue":"4","key":"10.3233\/JIFS-191105_ref16","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1016\/0968-090X(93)90002-W","article-title":"A neural network-based methodology for pavement crack detection and classification","volume":"1","author":"Kaseko","year":"1993","journal-title":"Transportation Research Part C Emerging Technologies"},{"issue":"4","key":"10.3233\/JIFS-191105_ref17","doi-asserted-by":"crossref","first-page":"552","DOI":"10.1061\/(ASCE)0733-947X(1994)120:4(552)","article-title":"Comparison of Traditional and Neural Classifers for Pavement-Crack Detection","volume":"120","author":"Kaseko","year":"1994","journal-title":"Journal of Transportation Engineering"},{"key":"10.3233\/JIFS-191105_ref18","doi-asserted-by":"crossref","unstructured":"Adaptive Road Crack Detection System by Pavement Classification, Sensors 11(12) (2011), 9628\u20139657.","DOI":"10.3390\/s111009628"},{"key":"10.3233\/JIFS-191105_ref19","doi-asserted-by":"crossref","first-page":"119","DOI":"10.3141\/2595-13","article-title":"Comparison of Supervised Classifcation Techniques for Vision-Based Pavement Crack Detection Transportation Research Record","volume":"2595","author":"Mokhtari","year":"2016","journal-title":"Journal of the Transportation Research Board"},{"key":"10.3233\/JIFS-191105_ref20","unstructured":"Nagahara H. , et al., SPIE Proceedings [SPIE The International Conference on Quality Control by Artificial Vision 2017 - Tokyo, Japan (Sunday 14 May 2017)] Thirteenth International Conference on Quality Control by Artificial Vision 2017 - A method based on machine learning using hand-crafted features for crack detection from asphalt pavement surface images, 10338 (2017), 103380I."},{"key":"10.3233\/JIFS-191105_ref21","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/j.autcon.2016.06.008","article-title":"Vision-based detection of loosened bolts using the Hough transform and support vector machines","volume":"71","author":"Cha","year":"2016","journal-title":"Automation in Construction"},{"issue":"6","key":"10.3233\/JIFS-191105_ref22","doi-asserted-by":"crossref","first-page":"743","DOI":"10.1061\/(ASCE)CP.1943-5487.0000245","article-title":"Unsupervised approach for autonomous pavement-defect detection and quantification using an inexpensive depth sensor","volume":"27","author":"Jahanshahi","year":"2012","journal-title":"Journal of Computing in Civil Engineering"},{"key":"10.3233\/JIFS-191105_ref23","doi-asserted-by":"crossref","unstructured":"Hoang N.D. and Nguyen Q.L. , A novel method for asphalt pavement crack classification based on image processing and machine learning, Engineering with Computers (2018).","DOI":"10.1007\/s00366-018-0611-9"},{"issue":"5","key":"10.3233\/JIFS-191105_ref24","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":"Computer-Aided Civil and Infrastructure Engineering"},{"key":"10.3233\/JIFS-191105_ref25","doi-asserted-by":"crossref","unstructured":"Kim B. and Cho S. , Automated Vision-Based Detection of Cracks on Concrete Surfaces Using a Deep Learning Technique, Sensors 18(10) (2018).","DOI":"10.3390\/s18103452"},{"issue":"99","key":"10.3233\/JIFS-191105_ref26","first-page":"1","article-title":"Automatic Pixel-Level Pavement Crack Detection Using Information of Multi-Scale Neighborhoods","volume":"PP","author":"Ai","year":"2018","journal-title":"IEEE Access"},{"issue":"6","key":"10.3233\/JIFS-191105_ref27","doi-asserted-by":"crossref","first-page":"1796","DOI":"10.3390\/s18061796","article-title":"Crack Damage Detection Method via Multiple Visual Features and Efficient Multi-Task Learning Model","volume":"18","author":"Baoxian","year":"2018","journal-title":"Sensors"},{"key":"10.3233\/JIFS-191105_ref28","first-page":"1","article-title":"A Kinect-Based Approach for 3D Pavement Surface Reconstruction and Cracking Recognition","author":"Yuming","year":"2018","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"issue":"99","key":"10.3233\/JIFS-191105_ref29","first-page":"1","article-title":"Pixel-Level Cracking Detection on 3D Asphalt Pavement Images Through Deep-Learning-Based CrackNet-V","volume":"PP","author":"Fei","year":"2019","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"key":"10.3233\/JIFS-191105_ref30","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1016\/j.autcon.2018.07.008","article-title":"Automatic recognition of asphalt pavement cracks using metaheuristic optimized edge detection algorithms and convolution neural network","volume":"94","author":"Hoang","year":"2018","journal-title":"Automation in Construction"},{"key":"10.3233\/JIFS-191105_ref31","first-page":"1","article-title":"Deep Crack: Learning Hierarchical Convolutional Features for Crack Detection","author":"Zou","year":"2018","journal-title":"IEEE Transactions on Image Processing"},{"issue":"2","key":"10.3233\/JIFS-191105_ref32","doi-asserted-by":"crossref","first-page":"04018001","DOI":"10.1061\/(ASCE)CP.1943-5487.0000736","article-title":"Unified Approach to Pavement Crack and Sealed Crack Detection Using Preclassification Based on Transfer Learning","volume":"32","author":"Zhang","year":"2018","journal-title":"Journal of Computing in Civil Engineering"},{"key":"10.3233\/JIFS-191105_ref33","doi-asserted-by":"crossref","first-page":"1031","DOI":"10.1016\/j.conbuildmat.2018.08.011","article-title":"Comparison of deep convolutional neural networks and edge detectors for image-based crack detection in concrete","volume":"186","author":"Sattar","year":"2018","journal-title":"Construction & Building Materials"},{"key":"10.3233\/JIFS-191105_ref34","doi-asserted-by":"crossref","unstructured":"Huang G. , et al., Densely Connected Convolutional Networks, (2016).","DOI":"10.1109\/CVPR.2017.243"},{"key":"10.3233\/JIFS-191105_ref35","unstructured":"Krizhevsky A. , Sutskever I. and Hinton G. , Image Net Classification with Deep Convolutional Neural Networks, Advances in Neural Information Processing Systems 25(2) (2012)."},{"key":"10.3233\/JIFS-191105_ref36","unstructured":"Abadi M. , et al., Tensor Flow: A system for large-scale machine learning, (2016)."},{"key":"10.3233\/JIFS-191105_ref37","doi-asserted-by":"crossref","unstructured":"Liu Y. , et al., Richer Convolutional Features for Edge Detection, (2016).","DOI":"10.1109\/CVPR.2017.622"},{"key":"10.3233\/JIFS-191105_ref38","unstructured":"Badrinarayanan V. , Kendall A. and Cipolla R. , Seg Net: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation, (2015)."},{"key":"10.3233\/JIFS-191105_ref39","doi-asserted-by":"crossref","unstructured":"Ronneberger O. , Fischer P. and Brox T. , U-Net: Convolutional Networks for Biomedical Image Segmentation, (2015).","DOI":"10.1007\/978-3-319-24574-4_28"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/JIFS-191105","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:42:00Z","timestamp":1777455720000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/JIFS-191105"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,4]]},"references-count":38,"journal-issue":{"issue":"1"},"URL":"https:\/\/doi.org\/10.3233\/jifs-191105","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1,4]]}}}