{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,3]],"date-time":"2026-08-03T17:04:05Z","timestamp":1785776645816,"version":"3.56.0"},"reference-count":44,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2026,1,6]],"date-time":"2026-01-06T00:00:00Z","timestamp":1767657600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Hainan Province Science and Technology Special Fund","award":["ZDYF2023GXJS159"],"award-info":[{"award-number":["ZDYF2023GXJS159"]}]},{"name":"Hainan Province Science and Technology Special Fund","award":["ZDYF2023GXJS168"],"award-info":[{"award-number":["ZDYF2023GXJS168"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Deep learning-based radar detection often suffers from poor cross-device generalization due to hardware heterogeneity. To address this, we propose a unified framework that combines rigorous calibration with adaptive temporal modeling. The method integrates three coordinated steps: (1) ensuring precise spatial alignment via improved Perspective-n-Point (PnP) calibration with closed-loop verification; (2) unifying signal statistics through multi-range bin calibration and chirp-wise Z-score standardization; and (3) enhancing feature consistency using a lightweight global\u2013temporal adapter (GTA) driven by global gating and three-point attention. By combining signal-level standardization with feature-level adaptation, our framework achieves 86.32% average precision (AP) on the ROD2021 dataset. It outperforms the E-RODNet baseline by 22.88 percentage points with a 0.96% parameter increase, showing strong generalization across diverse radar platforms.<\/jats:p>","DOI":"10.3390\/s26020373","type":"journal-article","created":{"date-parts":[[2026,1,6]],"date-time":"2026-01-06T15:09:36Z","timestamp":1767712176000},"page":"373","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["An Improved RODNet for Object Detection Based on Radar and Camera Fusion"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-7361-3951","authenticated-orcid":false,"given":"Manman","family":"Fan","sequence":"first","affiliation":[{"name":"School of Information and Communication Engineering, Hainan University, Haikou 570228, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6681-6489","authenticated-orcid":false,"given":"Xianpeng","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering, Hainan University, Haikou 570228, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6152-8955","authenticated-orcid":false,"given":"Mingcheng","family":"Fu","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering, Hainan University, Haikou 570228, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanqiu","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering, Hainan University, Haikou 570228, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4042-2609","authenticated-orcid":false,"given":"Yuehao","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering, Hainan University, Haikou 570228, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4769-3639","authenticated-orcid":false,"given":"Xiang","family":"Lan","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering, Hainan University, Haikou 570228, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Yin, T., Zhou, X., and Kr\u00e4henb\u00fchl, P. 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