{"id":24204,"date":"2026-06-12T15:52:02","date_gmt":"2026-06-12T13:52:02","guid":{"rendered":"https:\/\/fondation-grenoble-inp.fr\/?post_type=projet&#038;p=24204"},"modified":"2026-06-12T16:24:24","modified_gmt":"2026-06-12T14:24:24","slug":"jeu-de-donnees-lynred-mds","status":"publish","type":"projet","link":"http:\/\/fondation-grenoble-inp.fr\/en\/projet\/jeu-de-donnees-lynred-mds\/","title":{"rendered":"LYNRED-MDS Dataset"},"content":{"rendered":"<h2>Problematic :<\/h2>\n\n\n\n<p>How can multimodal imaging be used to improve the detection of road users?<\/p>\n\n\n\n<p>With recent advances in technology, particularly in advanced driver-assistance systems (ADAS), it has become essential to quickly detect road users in order to reduce the risk of accidents. However, conventional cameras reach their limits when visibility is poor (due to fog, glare, or at night). The combined use of RGB (color) and infrared images improves visibility and detection in all conditions, which represents a major advantage in the development of these systems.<\/p>\n\n\n\n<div style=\"height:18px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<h2>LYNRED-MDS Dataset<\/h2>\n\n\n\n<p>It is in this context that the<strong> <a href=\"http:\/\/fondation-grenoble-inp.fr\/en\/nos-actions\/chaire-deep-red\/\" target=\"_blank\" rel=\"noreferrer noopener\">Deep Red Chair<\/a><\/strong>, supported by the <a href=\"http:\/\/fondation-grenoble-inp.fr\/en\/\" target=\"_blank\" rel=\"noreferrer noopener\"><strong>Grenoble INP Foundation<\/strong><\/a> through sponsorship from <a href=\"https:\/\/www.lynred.com\/fr\" target=\"_blank\" rel=\"noreferrer noopener\"><strong>LYNRED<\/strong><\/a>, created the LYNRED-MDS dataset, which combines infrared and RGB imagery to provide more diverse and realistic data for the development of more advanced computer vision systems. Unlike existing datasets, LYNRED-MDS contains <strong>4,000 image pairs<\/strong>, acquired in a wide variety of environments\u2014urban, rural, and mountainous areas\u2014as well as under diverse seasonal and temperature conditions, to better represent the diversity of possible situations on the road.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" width=\"790\" height=\"379\" src=\"https:\/\/fondation-grenoble-inp.fr\/wp-content\/uploads\/2026\/06\/Jeu-de-donnees-LYNRED-MDS.png\" alt=\"\" class=\"wp-image-24205\" srcset=\"https:\/\/fondation-grenoble-inp.fr\/wp-content\/uploads\/2026\/06\/Jeu-de-donnees-LYNRED-MDS.png 790w, https:\/\/fondation-grenoble-inp.fr\/wp-content\/uploads\/2026\/06\/Jeu-de-donnees-LYNRED-MDS-300x144.png 300w, https:\/\/fondation-grenoble-inp.fr\/wp-content\/uploads\/2026\/06\/Jeu-de-donnees-LYNRED-MDS-768x368.png 768w, https:\/\/fondation-grenoble-inp.fr\/wp-content\/uploads\/2026\/06\/Jeu-de-donnees-LYNRED-MDS-18x9.png 18w\" sizes=\"(max-width: 790px) 100vw, 790px\" \/><figcaption> Three examples of multimodal data extracted from the LYNRED-MDS dataset<\/figcaption><\/figure>\n\n\n\n<figure class=\"wp-block-image size-full is-resized\"><img loading=\"lazy\" src=\"https:\/\/fondation-grenoble-inp.fr\/wp-content\/uploads\/2026\/06\/Jeu-de-donnees-LYNRED-MDS-2.png\" alt=\"\" class=\"wp-image-24206\" width=\"793\" height=\"133\" srcset=\"https:\/\/fondation-grenoble-inp.fr\/wp-content\/uploads\/2026\/06\/Jeu-de-donnees-LYNRED-MDS-2.png 793w, https:\/\/fondation-grenoble-inp.fr\/wp-content\/uploads\/2026\/06\/Jeu-de-donnees-LYNRED-MDS-2-300x50.png 300w, https:\/\/fondation-grenoble-inp.fr\/wp-content\/uploads\/2026\/06\/Jeu-de-donnees-LYNRED-MDS-2-768x129.png 768w, https:\/\/fondation-grenoble-inp.fr\/wp-content\/uploads\/2026\/06\/Jeu-de-donnees-LYNRED-MDS-2-18x3.png 18w\" sizes=\"(max-width: 793px) 100vw, 793px\" \/><figcaption><strong>Specific characteristics of datasets for multiple RGB-T datasets<\/strong><\/figcaption><\/figure>\n\n\n\n<div style=\"height:18px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<h2>Image acquisition<\/h2>\n\n\n\n<p>The images in the LYNRED-MDS dataset were acquired using two cameras mounted on the roof of a test vehicle. These cameras were positioned as shown in the diagram below and synchronized to capture images at the same moment, at a rate of <strong>30 frames per second<\/strong>. However, only one image is retained every three seconds to maximize scene diversity.<\/p>\n\n\n\n<div class=\"wp-block-image\"><figure class=\"aligncenter size-full\"><img loading=\"lazy\" width=\"397\" height=\"416\" src=\"https:\/\/fondation-grenoble-inp.fr\/wp-content\/uploads\/2026\/06\/Jeu-de-donnees-LYNRED-MDS-3.png\" alt=\"\" class=\"wp-image-24207\" srcset=\"https:\/\/fondation-grenoble-inp.fr\/wp-content\/uploads\/2026\/06\/Jeu-de-donnees-LYNRED-MDS-3.png 397w, https:\/\/fondation-grenoble-inp.fr\/wp-content\/uploads\/2026\/06\/Jeu-de-donnees-LYNRED-MDS-3-286x300.png 286w, https:\/\/fondation-grenoble-inp.fr\/wp-content\/uploads\/2026\/06\/Jeu-de-donnees-LYNRED-MDS-3-11x12.png 11w\" sizes=\"(max-width: 397px) 100vw, 397px\" \/><figcaption><strong>Schematic representation of the dual-stereo setup used to acquire the dataset.<\/strong><\/figcaption><\/figure><\/div>\n\n\n\n<p><em>For the object detection task, only the pair of cameras on the left is used. To ensure that images are properly aligned within the region of interest, the sensors are oriented so that objects located at a great distance (in this case, more than 10 meters away) appear aligned across all sensors.<\/em><br><em>(a) Diagram of the acquisition system.<\/em><br><em>(b) Photograph of the actual device installed on the vehicle\u2019s roof.<\/em><\/p>\n\n\n\n<div style=\"height:18px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<h2>Access to data<\/h2>\n\n\n\n<p>The dataset is available for free <a href=\"https:\/\/www.lynred.com\/lynred-mobility-dataset\" target=\"_blank\" rel=\"noreferrer noopener\"><strong>at this link<\/strong><\/a>.<\/p>\n\n\n\n<div style=\"height:18px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<h2>Reference<\/h2>\n\n\n\n<p><a href=\"https:\/\/ieeexplore.ieee.org\/document\/11533396\">https:\/\/ieeexplore.ieee.org\/document\/11533396<\/a><\/p>\n\n\n\n<p>L. Arbez, J. Matias, X. Breni\u00e8re, J. Chanussot and R. Phlypo, \u00ab\u00a0Descriptor: LYNRED Mobility Dataset Multimodal Detection Subset (LYNRED-MDS),\u00a0\u00bb in <em>IEEE Data Descriptions<\/em>, vol. 3, pp. 413-421, 2026, doi: 10.1109\/IEEEDATA.2026.3695478<\/p>","protected":false},"excerpt":{"rendered":"<p>Un projet visant \u00e0 d\u00e9tecter rapidement les usagers de la route gr\u00e2ce \u00e0 l&rsquo;imagerie infrarouge, afin de r\u00e9duire le risque d\u2019accident.<\/p>","protected":false},"featured_media":24205,"template":"","acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v18.0 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Jeu de donn\u00e9es LYNRED-MDS - Fondation Grenoble INP<\/title>\n<meta name=\"description\" content=\"Un projet visant \u00e0 d\u00e9tecter rapidement les usagers de la route gr\u00e2ce \u00e0 l\u2019imagerie multimodale, afin de r\u00e9duire le risque d\u2019accident.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"http:\/\/fondation-grenoble-inp.fr\/en\/projet\/jeu-de-donnees-lynred-mds\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Jeu de donn\u00e9es LYNRED-MDS - 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