{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T18:07:20Z","timestamp":1784311640629,"version":"3.55.0"},"reference-count":33,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2024,6,13]],"date-time":"2024-06-13T00:00:00Z","timestamp":1718236800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["51805078"],"award-info":[{"award-number":["51805078"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Thanks to the line-scanning camera, the measurement method based on line-scanning stereo vision has high optical accuracy, data transmission efficiency, and a wide field of vision. It is more suitable for continuous operation and high-speed transmission of industrial product detection sites. However, the one-dimensional imaging characteristics of the line-scanning camera cause motion distortion during image data acquisition, which directly affects the accuracy of detection. Effectively reducing the influence of motion distortion is the primary problem to ensure detection accuracy. To obtain the two-dimensional color image and three-dimensional contour data of the heavy rail surface at the same time, a binocular color line-scanning stereo vision system is designed to collect the heavy rail surface data combined with the bright field illumination of the symmetrical linear light source. Aiming at the image motion distortion caused by system installation error and collaborative acquisition frame rate mismatch, this paper uses the checkerboard target and two-step cubature Kalman filter algorithm to solve the nonlinear parameters in the motion distortion model, estimate the real motion, and correct the image information. The experiments show that the accuracy of the data contained in the image is improved by 57.3% after correction.<\/jats:p>","DOI":"10.3390\/jimaging10060144","type":"journal-article","created":{"date-parts":[[2024,6,13]],"date-time":"2024-06-13T10:41:03Z","timestamp":1718275263000},"page":"144","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["A Binocular Color Line-Scanning Stereo Vision System for Heavy Rail Surface Detection and Correction Method of Motion Distortion"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4089-4681","authenticated-orcid":false,"given":"Chao","family":"Wang","sequence":"first","affiliation":[{"name":"School of Transportation, Ludong University, Yantai 264025, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weixi","family":"Luo","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering & Automation, Northeastern University, Shenyang 110819, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Menghui","family":"Niu","sequence":"additional","affiliation":[{"name":"Beijing Institute of Control and Electronics Technology, Beijing 100045, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiqiang","family":"Li","sequence":"additional","affiliation":[{"name":"School of Transportation, Ludong University, Yantai 264025, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kechen","family":"Song","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering & Automation, Northeastern University, Shenyang 110819, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1109\/TII.2019.2917522","article-title":"Surface defect detection via entity sparsity pursuit with intrinsic priors","volume":"16","author":"Wang","year":"2020","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"656","DOI":"10.1109\/TIM.2018.2853958","article-title":"A coarse-to-fine model for rail surface defect detection","volume":"68","author":"Yu","year":"2019","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1007\/BF01211850","article-title":"Automated visual inspection of rolled metal surfaces","volume":"3","author":"Piironen","year":"1990","journal-title":"Mach. Vis. Appl."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"858","DOI":"10.1016\/j.apsusc.2013.09.002","article-title":"A noise robust method based on completed local binary patterns for hot-rolled steel strip surface defects","volume":"285","author":"Song","year":"2013","journal-title":"Appl. Surf. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"5803","DOI":"10.1016\/j.ijleo.2014.07.070","article-title":"On-line conveyor belts inspection based on machine vision","volume":"125","author":"Yang","year":"2014","journal-title":"Optik"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Wang, Z.Z., and Wang, S.M. (2015, January 12\u201313). Research of method for detection of rail fastener defects based on machine vision. Proceedings of the 4th International Conference on Mechatronics, Materials, Chemistry and Computer Engineering, Xi\u2019an, China.","DOI":"10.2991\/icmmcce-15.2015.547"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"7448","DOI":"10.1109\/TII.2019.2958826","article-title":"PGA-Net: Pyramid feature fusion and global context attention network for automated surface defect detection","volume":"16","author":"Dong","year":"2019","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Cheng, Y., Deng, H.G., and Feng, Y.X. (2020, January 12\u201314). Effects of faster region-based convolutional neural network on the detection efficiency of rail defects under machine vision. Proceedings of the IEEE 5th Information Technology and Mechatronics Engineering Conference, Chongqing, China.","DOI":"10.1109\/ITOEC49072.2020.9141787"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"106000","DOI":"10.1016\/j.optlaseng.2019.106000","article-title":"Saliency detection for strip steel surface defects using multiple constraints and improved texture features","volume":"128","author":"Song","year":"2020","journal-title":"Opt. Lasers Eng."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"105936","DOI":"10.1016\/j.optlaseng.2019.105936","article-title":"Unified detection method of aluminium profile surface defects: Common and rare defect categories","volume":"126","author":"Zhang","year":"2020","journal-title":"Opt. Lasers Eng."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"022012","DOI":"10.1088\/1742-6596\/1748\/2\/022012","article-title":"A detection system for rail defects based on machine vision","volume":"1748","author":"Zhou","year":"2021","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"655","DOI":"10.1177\/03611981211019034","article-title":"Automatic rail surface defects inspection based on Mask R-CNN","volume":"2675","author":"Guo","year":"2021","journal-title":"Transp. Res. Rec."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"9667","DOI":"10.1109\/TII.2022.3233654","article-title":"Shape consistent one-shot unsupervised domain adaptation for rail surface defect segmentation","volume":"19","author":"Ma","year":"2023","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"107674","DOI":"10.1016\/j.optlaseng.2023.107674","article-title":"Unsupervised surface defect detection of aluminum sheets with combined bright-field and dark-field illumination","volume":"168","author":"Sun","year":"2023","journal-title":"Opt. Lasers Eng."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1016\/j.neucom.2021.05.115","article-title":"A flexible free-space detection system based on stereo vision","volume":"485","author":"Xie","year":"2022","journal-title":"Neurocomputing"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"106086","DOI":"10.1016\/j.optlaseng.2020.106086","article-title":"Large-scale structured light 3D shape measurement with reverse photography","volume":"130","author":"Xiao","year":"2020","journal-title":"Opt. Lasers Eng."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Sun, B., Zhu, J.G., Yang, L.H., Yang, S.R., and Guo, Y. (2016). Sensor for in-motion continuous 3D shape measurement based on dual line-scan cameras. Sensors, 16.","DOI":"10.3390\/s16111949"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Ma, Y.P., Li, Q.W., Chu, L.L., Zhou, Y.Q., and Xu, C. (2021). Real-time detection and spatial localization of insulators for UAV inspection based on binocular stereo vision. Remote Sens., 13.","DOI":"10.3390\/rs13020230"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"73890F","DOI":"10.1117\/12.823837","article-title":"Development of a high resolution pattern projection system using linescan cameras","volume":"7389","author":"Denkena","year":"2009","journal-title":"Proc. SPIE"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1258","DOI":"10.1109\/TIM.2014.2364105","article-title":"A structured light approach for 3-D surface reconstruction with a stereo line-scan system","volume":"64","author":"Lilienblum","year":"2015","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_21","first-page":"2271","article-title":"Unsupervised saliency detection of rail surface defects using stereoscopic images","volume":"17","author":"Niu","year":"2021","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Wu, C.Y., Yang, L., Luo, Z., and Jiang, W.S. (2022). Linear laser scanning measurement method tracking by a binocular vision. Sensors, 22.","DOI":"10.3390\/s22093572"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"105817","DOI":"10.1016\/j.optlaseng.2019.105817","article-title":"A calibration method for binocular stereo vision sensor with short-baseline based on 3D flexible control field","volume":"124","author":"Yang","year":"2020","journal-title":"Opt. Lasers Eng."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1349","DOI":"10.1016\/j.patrec.2012.03.014","article-title":"Simultaneous line matching and epipolar geometry estimation based on the intersection context of coplanar line pairs","volume":"33","author":"Kim","year":"2012","journal-title":"Pattern Recognit. Lett."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"163917","DOI":"10.1016\/j.ijleo.2019.163917","article-title":"Binocular stereo vision calibration based on constrained sparse beam adjustment algorithm","volume":"208","author":"Guo","year":"2020","journal-title":"Optik"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"15205","DOI":"10.1364\/OE.23.015205","article-title":"Improved camera calibration method based on perpendicularity compensation for binocular stereo vision measurement system","volume":"23","author":"Jia","year":"2015","journal-title":"Opt. Express"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1016\/S0923-5965(03)00013-4","article-title":"Fast view interpolation of stereo images using image gradient and disparity triangulation","volume":"18","author":"Park","year":"2003","journal-title":"Signal Process.-Image Commun."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.neucom.2014.11.087","article-title":"Local iterative DLT soft-computing vs. interval-valued stereo calibration and triangulation with uncertainty bounding in 3D reconstruction","volume":"167","author":"Otero","year":"2015","journal-title":"Neurocomputing"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"113001","DOI":"10.1016\/j.measurement.2023.113001","article-title":"Research progress of laser triangulation on-machine measurement technology for complex surface: A review","volume":"216","author":"Ding","year":"2023","journal-title":"Measurement"},{"key":"ref_30","first-page":"8742920","article-title":"Literature survey on stereo vision disparity map algorithms","volume":"2016","author":"Hamzah","year":"2015","journal-title":"J. Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1023\/A:1014573219977","article-title":"A taxonomy and evaluation of dense two-frame stereo correspondence algorithm","volume":"47","author":"Scharstein","year":"2002","journal-title":"Int. J. Comput. Vis."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"106774","DOI":"10.1016\/j.optlaseng.2021.106774","article-title":"H3D Surface reconstruction of transparent objects using laser scanning with LTFtF method","volume":"148","author":"He","year":"2022","journal-title":"Opt. Lasers Eng."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1109\/TPAMI.2007.1166","article-title":"Stereo processing by semiglobal matching and mutual information","volume":"30","author":"Hirschmuller","year":"2008","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/10\/6\/144\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:58:10Z","timestamp":1760108290000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/10\/6\/144"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,13]]},"references-count":33,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2024,6]]}},"alternative-id":["jimaging10060144"],"URL":"https:\/\/doi.org\/10.3390\/jimaging10060144","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,13]]}}}