{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T15:10:15Z","timestamp":1777129815700,"version":"3.51.4"},"reference-count":17,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2021,8,12]],"date-time":"2021-08-12T00:00:00Z","timestamp":1628726400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"M2SINUM","award":["18P03390\/18E01750\/18P02733"],"award-info":[{"award-number":["18P03390\/18E01750\/18P02733"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>For smart mobility, autonomous vehicles, and advanced driver-assistance systems (ADASs), perception of the environment is an important task in scene analysis and understanding. Better perception of the environment allows for enhanced decision making, which, in turn, enables very high-precision actions. To this end, we introduce in this work a new real-time deep learning approach for 3D multi-object detection for smart mobility not only on roads, but also on railways. To obtain the 3D bounding boxes of the objects, we modified a proven real-time 2D detector, YOLOv3, to predict 3D object localization, object dimensions, and object orientation. Our method has been evaluated on KITTI\u2019s road dataset as well as on our own hybrid virtual road\/rail dataset acquired from the video game Grand Theft Auto (GTA) V. The evaluation of our method on these two datasets shows good accuracy, but more importantly that it can be used in real-time conditions, in road and rail traffic environments. Through our experimental results, we also show the importance of the accuracy of prediction of the regions of interest (RoIs) used in the estimation of 3D bounding box parameters.<\/jats:p>","DOI":"10.3390\/jimaging7080145","type":"journal-article","created":{"date-parts":[[2021,8,12]],"date-time":"2021-08-12T02:40:14Z","timestamp":1628736014000},"page":"145","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Real-Time 3D Multi-Object Detection and Localization Based on Deep Learning for Road and Railway Smart Mobility"],"prefix":"10.3390","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0313-3859","authenticated-orcid":false,"given":"Antoine","family":"Mauri","sequence":"first","affiliation":[{"name":"Normandie Univ, UNIROUEN, ESIGELEC, IRSEEM, 76000 Rouen, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6230-2966","authenticated-orcid":false,"given":"Redouane","family":"Khemmar","sequence":"additional","affiliation":[{"name":"Normandie Univ, UNIROUEN, ESIGELEC, IRSEEM, 76000 Rouen, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4037-2880","authenticated-orcid":false,"given":"Benoit","family":"Decoux","sequence":"additional","affiliation":[{"name":"Normandie Univ, UNIROUEN, ESIGELEC, IRSEEM, 76000 Rouen, France"}]},{"given":"Madjid","family":"Haddad","sequence":"additional","affiliation":[{"name":"Haddad is with SEGULA Technologies, 19 rue d\u2019Arras, 92000 Nanterre, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1078-5043","authenticated-orcid":false,"given":"R\u00e9mi","family":"Boutteau","sequence":"additional","affiliation":[{"name":"Normandie Univ, UNIROUEN, UNILEHAVRE, INSA Rouen, LITIS, 76000 Rouen, France"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zhao, M., Mammeri, A., and Boukerche, A. (2015, January 27\u201329). Distance measurement system for smart vehicles. Proceedings of the 2015 7th International Conference on New Technologies, Mobility and Security (NTMS), Paris, France.","DOI":"10.1109\/NTMS.2015.7266486"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1377","DOI":"10.1109\/TIM.2007.900126","article-title":"Real-time tree-foliage surface estimation using a ground laser scanner","volume":"56","author":"Tresanchez","year":"2007","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"786403","DOI":"10.1117\/12.872313","article-title":"Harmonic distortion free distance estimation in ToF camera","volume":"Volume 7864","author":"Kang","year":"2011","journal-title":"Three-Dimensional Imaging, Interaction, and Measurement"},{"key":"ref_4","unstructured":"Qi, C.R., Su, H., Mo, K., and Guibas, L.J. (2017). 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Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Long Beach, CA, USA.","DOI":"10.1109\/CVPRW.2019.00161"}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/7\/8\/145\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:44:30Z","timestamp":1760165070000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/7\/8\/145"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,12]]},"references-count":17,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2021,8]]}},"alternative-id":["jimaging7080145"],"URL":"https:\/\/doi.org\/10.3390\/jimaging7080145","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,8,12]]}}}