{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,24]],"date-time":"2024-08-24T07:47:07Z","timestamp":1724485627854},"reference-count":30,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,8,12]],"date-time":"2023-08-12T00:00:00Z","timestamp":1691798400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,8,12]],"date-time":"2023-08-12T00:00:00Z","timestamp":1691798400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"University Natural Science Research Project of Anhui Province [China]","award":["KJ2019A0049","KJ2020A0238"]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SIViP"],"published-print":{"date-parts":[[2024,2]]},"DOI":"10.1007\/s11760-023-02696-8","type":"journal-article","created":{"date-parts":[[2023,8,12]],"date-time":"2023-08-12T07:01:36Z","timestamp":1691823696000},"page":"81-89","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Particle recognition and shape parameter detection based on deep learning"],"prefix":"10.1007","volume":"18","author":[{"given":"Xuan","family":"Li","sequence":"first","affiliation":[]},{"given":"Zhou","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Xinyu","family":"Tao","sequence":"additional","affiliation":[]},{"given":"Xiaojie","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Yufeng","family":"Han","sequence":"additional","affiliation":[]},{"given":"Xutao","family":"Mo","sequence":"additional","affiliation":[]},{"given":"Xianshan","family":"Huang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,8,12]]},"reference":[{"key":"2696_CR1","volume-title":"An Introduction to Soils for Environmental Professionals","author":"DL Winegardner","year":"1995","unstructured":"Winegardner, D.L.: An Introduction to Soils for Environmental Professionals. Lewis Publishers (1995)"},{"key":"2696_CR2","doi-asserted-by":"publisher","first-page":"256","DOI":"10.1016\/j.enggeo.2017.02.015","volume":"220","author":"HS Suh","year":"2017","unstructured":"Suh, H.S., et al.: Quantification of bulk form and angularity of particle with correlation of shear strength and packing density in sands. Eng. Geol. 220, 256\u2013265 (2017)","journal-title":"Eng. Geol."},{"issue":"12","key":"2696_CR3","doi-asserted-by":"publisher","first-page":"04016071","DOI":"10.1061\/(ASCE)GT.1943-5606.0001569","volume":"142","author":"FN Altuhafi","year":"2016","unstructured":"Altuhafi, F.N., Coop, M.R., Georgiannou, V.N.: Effect of particle shape on the mechanical behavior of natural sands. J. Geotech. Geoenviron. Eng. 142(12), 04016071 (2016). https:\/\/doi.org\/10.1061\/(ASCE)GT.1943-5606.0001569","journal-title":"J. Geotech. Geoenviron. Eng."},{"issue":"1","key":"2696_CR4","doi-asserted-by":"publisher","first-page":"106142","DOI":"10.1016\/j.enggeo.2021.106142","volume":"288","author":"Y Kim","year":"2021","unstructured":"Kim, Y., Yun, T.S.: How to classify sand types: a deep learning approach. Eng. Geol. 288(1), 106142 (2021)","journal-title":"Eng. Geol."},{"key":"2696_CR5","doi-asserted-by":"publisher","first-page":"1521","DOI":"10.1007\/s11440-022-01464-1","volume":"17","author":"Y Kim","year":"2022","unstructured":"Kim, Y., et al.: Determination of shape parameters of sands: a deep learning approach. Acta Geotech. 17, 1521\u20131531 (2022)","journal-title":"Acta Geotech."},{"key":"2696_CR6","doi-asserted-by":"publisher","first-page":"196","DOI":"10.1016\/j.jmps.2015.08.001","volume":"84","author":"J Yang","year":"2015","unstructured":"Yang, J., Luo, X.D.: Exploring the relationship between critical state and particle shape for granular materials. J. Mech. Phys. Solids 84, 196\u2013213 (2015). https:\/\/doi.org\/10.1016\/j.jmps.2015.08.001","journal-title":"J. Mech. Phys. Solids"},{"key":"2696_CR7","doi-asserted-by":"publisher","first-page":"412","DOI":"10.1016\/j.measurement.2018.09.012","volume":"131","author":"A Kumar","year":"2019","unstructured":"Kumar, A., Ghosh, S.K.: Size distribution analysis of wear debris generated in HEMM engine oil for reliability assessment: a statistical approach. Measurement 131, 412\u2013418 (2019). https:\/\/doi.org\/10.1016\/j.measurement.2018.09.012","journal-title":"Measurement"},{"key":"2696_CR8","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1016\/j.powtec.2019.08.028","volume":"356","author":"Q Sun","year":"2019","unstructured":"Sun, Q., Zheng, J., Li, C.: Improved watershed analysis for segmenting contacting particles of coarse granular soils in volumetric images. Powder Technol. 356, 295\u2013303 (2019)","journal-title":"Powder Technol."},{"key":"2696_CR9","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1016\/j.measurement.2019.03.046","volume":"140","author":"S Banerjee","year":"2019","unstructured":"Banerjee, S., Chakraborti, P.C., Saha, S.K.: An automated methodology for grain segmentation and grain size measurement from optical micrographs. Measurement 140, 142\u2013150 (2019)","journal-title":"Measurement"},{"key":"2696_CR10","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1016\/j.cageo.2019.05.009","volume":"130","author":"J Maitre","year":"2019","unstructured":"Maitre, J., Bouchard, K., Bedard, L.P.: Mineral grains recognition using computer vision and machine learning. Comput. Geosci. 130, 84\u201393 (2019)","journal-title":"Comput. Geosci."},{"issue":"8","key":"2696_CR11","doi-asserted-by":"publisher","first-page":"974","DOI":"10.1080\/02726351.2018.1496958","volume":"37","author":"JH Yang","year":"2018","unstructured":"Yang, J.H., Fang, H.Y., Chen, S.J.: Development of particle size and shape measuring system for machine-made sand. Part. Sci. Technol. 37(8), 974\u2013980 (2018)","journal-title":"Part. Sci. Technol."},{"issue":"12","key":"2696_CR12","doi-asserted-by":"publisher","first-page":"124001","DOI":"10.1088\/1361-6501\/aadc3f","volume":"29","author":"X Wu","year":"2018","unstructured":"Wu, X., et al.: Digital holographic sizer for coal powder size distribution measurement: preliminary simulation and experiment. Meas. Sci. Technol. 29(12), 124001 (2018)","journal-title":"Meas. Sci. Technol."},{"key":"2696_CR13","doi-asserted-by":"publisher","first-page":"107867","DOI":"10.1016\/j.measurement.2020.107867","volume":"160","author":"X Yang","year":"2020","unstructured":"Yang, X., Ren, T., Tan, L.: Size distribution measurement of coal fragments using digital imaging processing. Measurement 160, 107867 (2020)","journal-title":"Measurement"},{"key":"2696_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.cageo.2019.03.005","volume":"128","author":"A Hr","year":"2019","unstructured":"Hr, A., et al.: Automated segmentation of gravel particles from depth images of gravel-soil mixtures - ScienceDirect. Comput. Geosci. 128, 1\u201310 (2019)","journal-title":"Comput. Geosci."},{"issue":"8","key":"2696_CR15","doi-asserted-by":"publisher","first-page":"085202","DOI":"10.1088\/1361-6501\/ab7283","volume":"31","author":"NH Kling","year":"2020","unstructured":"Kling, N.H., et al.: Dual-plane stereo-astigmatism\u2014A novel method to determine the full velocity gradient tensor in planar domain. Meas. Sci. Technol. 31(8), 085202 (2020)","journal-title":"Meas. Sci. Technol."},{"key":"2696_CR16","doi-asserted-by":"publisher","first-page":"136","DOI":"10.1016\/j.measurement.2019.04.071","volume":"143","author":"IC Engin","year":"2019","unstructured":"Engin, I.C., Maerz, N.H.: Size distribution analysis of aggregates using LiDAR scan data and an alternate algorithm. Measurement 143, 136 (2019)","journal-title":"Measurement"},{"key":"2696_CR17","doi-asserted-by":"publisher","first-page":"105358","DOI":"10.1016\/j.enggeo.2019.105358","volume":"263","author":"W Zheng","year":"2019","unstructured":"Zheng, W., et al.: Characterization of two- and three-dimensional morphological properties of fragmented sand grains. Eng. Geol. 263, 105358 (2019)","journal-title":"Eng. Geol."},{"key":"2696_CR18","doi-asserted-by":"publisher","first-page":"156","DOI":"10.1016\/j.powtec.2019.05.025","volume":"353","author":"Z Liang","year":"2019","unstructured":"Liang, Z., Nie, Z., An, A., et al.: A particle shape extraction and evaluation method using a deep convolutional neural network and digital image processing. Powder Technol. 353, 156\u2013170 (2019)","journal-title":"Powder Technol."},{"issue":"1","key":"2696_CR19","doi-asserted-by":"publisher","first-page":"015406","DOI":"10.1088\/1361-6501\/abae90","volume":"32","author":"J Li","year":"2021","unstructured":"Li, J., Shao, S., Hong, J.: Machine learning shadowgraph for particle size and shape characterization. Meas. Sci. Technol. 32(1), 015406 (2021)","journal-title":"Meas. Sci. Technol."},{"key":"2696_CR20","doi-asserted-by":"publisher","first-page":"e00198","DOI":"10.1016\/j.geodrs.2018.e00198","volume":"16","author":"J Padarian","year":"2018","unstructured":"Padarian, J., Minasny, B., Mcbratney, A.B.: Using deep learning to predict soil properties from regional spectral data. Geoderma Region. 16, e00198 (2018)","journal-title":"Geoderma Region."},{"issue":"4","key":"2696_CR21","doi-asserted-by":"publisher","first-page":"640","DOI":"10.1109\/TPAMI.2016.2572683","volume":"39","author":"E Shelhamer","year":"2017","unstructured":"Shelhamer, E., Long, J., Darrell, T.: Fully convolutional networks for semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(4), 640\u2013651 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"2696_CR22","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In International Conference on Medical Image Computing and Computer-Assisted Intervention, Vol. 9351, pp. 234\u2013241. Springer Verlag (2015)","DOI":"10.1007\/978-3-319-24574-4_28"},{"issue":"6","key":"2696_CR23","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","volume":"39","author":"S Ren","year":"2017","unstructured":"Ren, S., He, K., Girshick, R., et al.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137\u20131149 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"2696_CR24","unstructured":"Redmon, J., Farhadi A.: YOLOv3: An Incremental Improvement. arXiv e-prints (2018)"},{"issue":"2","key":"2696_CR25","doi-asserted-by":"publisher","first-page":"386","DOI":"10.1109\/TPAMI.2018.2844175","volume":"42","author":"K He","year":"2020","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., et al.: Mask R-CNN. IEEE Trans. Pattern Anal. Mach. Intell. 42(2), 386\u2013397 (2020)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"2696_CR26","doi-asserted-by":"crossref","unstructured":"Chen, H., et al.: BlendMask: top-down meets bottom-up for instance segmentation. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) IEEE (2020)","DOI":"10.1109\/CVPR42600.2020.00860"},{"key":"2696_CR27","unstructured":"Krizhevsky, A., Sutskever I., Hinton G.: ImageNet classification with deep convolutional neural networks. Adv. Neural Inform. Process. Syst. 25(2) (2012)"},{"key":"2696_CR28","doi-asserted-by":"publisher","first-page":"740","DOI":"10.1007\/978-3-319-10602-1_48","volume-title":"Computer Vision \u2013 ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V","author":"T-Y Lin","year":"2014","unstructured":"Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Piotr Doll\u00e1r, C., Zitnick, L.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, Bernt, Tuytelaars, T. (eds.) Computer Vision \u2013 ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V, pp. 740\u2013755. Springer International Publishing, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10602-1_48"},{"issue":"6","key":"2696_CR29","doi-asserted-by":"publisher","first-page":"679","DOI":"10.1109\/TPAMI.1986.4767851","volume":"8","author":"J Canny","year":"1986","unstructured":"Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. PAMI 8(6), 679\u2013698 (1986)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell. PAMI"},{"issue":"2","key":"2696_CR30","doi-asserted-by":"publisher","first-page":"176","DOI":"10.1109\/83.217222","volume":"2","author":"LU Vincent","year":"1993","unstructured":"Vincent, L.U.: Morphological gray scale reconstruction in image analysis: applications and efficient algorithms. IEEE Trans. Image Process. 2(2), 176\u2013201 (1993)","journal-title":"IEEE Trans. Image Process."}],"container-title":["Signal, Image and Video Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-023-02696-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11760-023-02696-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-023-02696-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,25]],"date-time":"2024-01-25T15:34:34Z","timestamp":1706196874000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11760-023-02696-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,12]]},"references-count":30,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2024,2]]}},"alternative-id":["2696"],"URL":"https:\/\/doi.org\/10.1007\/s11760-023-02696-8","relation":{},"ISSN":["1863-1703","1863-1711"],"issn-type":[{"value":"1863-1703","type":"print"},{"value":"1863-1711","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,12]]},"assertion":[{"value":"16 June 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 May 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 July 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 August 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interest or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval and consent to participate"}},{"value":"Applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}]}}