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Express"],"published-print":{"date-parts":[[2026,3,9]]},"abstract":"<jats:p>We evaluate a three-dimensional (3D) object detection system for operation in turbid water based on 3D profilometric Integral Imaging (InIm) and a deep neural network. While conventional InIm computational reconstruction provides the two-dimensional (2D) slices of the 3D scene at specific 2D depth planes, 3D profilometry allows visualization of the 3D surface from unique perspectives. In the proposed method, we develop a deep neural network-based red, green, blue-depth (RGB-D) object detection framework using passive 3D profilometry under turbid conditions. An image sensor on a moving platform captures multiple 2D perspective images of the 3D scene, from which a depth map is statistically estimated. The captured perspective image and the estimated depth map are then fused to generate a four-channel RGB-D image for 3D object detection in turbidity. Comparative experiments are conducted and demonstrate that the proposed 3D profilometry-based approach outperforms both 2D imaging and conventional 3D InIm-based reconstruction across various turbidity levels evaluated. To the best of our knowledge, this is the first report on InIm 3D profilometry for object detection in turbid water.<\/jats:p>","DOI":"10.1364\/oe.583889","type":"journal-article","created":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T07:00:08Z","timestamp":1771484408000},"page":"9238","update-policy":"https:\/\/doi.org\/10.1364\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["3D profilometric object detection in turbid water using integral imaging and deep neural networks"],"prefix":"10.1364","volume":"34","author":[{"given":"Alex","family":"Maric","sequence":"first","affiliation":[{"id":[{"id":"https:\/\/ror.org\/02der9h97","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Shen","sequence":"additional","affiliation":[{"name":"University of Hartford"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gregory","family":"Aschenbrenner","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/02der9h97","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3612-2873","authenticated-orcid":true,"given":"Bahram","family":"Javidi","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/02der9h97","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"285","published-online":{"date-parts":[[2026,3,6]]},"reference":[{"key":"oe-34-5-9238-R1","doi-asserted-by":"publisher","first-page":"123638","DOI":"10.1109\/ACCESS.2019.2932611","type":"journal-article","volume":"7","author":"Yang","year":"2019","journal-title":"IEEE Access"},{"key":"oe-34-5-9238-R2","doi-asserted-by":"publisher","first-page":"1587","DOI":"10.1049\/iet-ipr.2019.0117","type":"journal-article","volume":"13","author":"Jarina","year":"2019","journal-title":"IET Sig. 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