{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T15:54:51Z","timestamp":1778601291557,"version":"3.51.4"},"reference-count":52,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T00:00:00Z","timestamp":1775174400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Underwater sonar object detection is challenging because targets are often small, boundaries are blurred, background clutter is strong, and labeled sonar data are limited. To address these issues, we propose T2C-DETR, a detector built on RT-DETR with three task-oriented improvements: (i) a Transformer\u2013Convolution dual-channel backbone (TCDCNet) for complementary global-context and local-detail modeling, (ii) a Noise Filtering Module (NFM) inserted before neck fusion to suppress noise-dominated activations, and (iii) a stage-wise transfer-learning strategy tailored to small sonar datasets. We evaluate the method under three pre-training sources (COCO 2017, DOTA, and an infrared dataset) and then fine-tune on a self-built sonar dataset. Experimental results show that T2C-DETR achieves AP50 of 97.8%, 98.2%, and 98.5% at 72\u201373 FPS, consistently outperforming the RT-DETR baseline, YOLOv5-Imp, and MLFFNet in the accuracy\u2013speed trade-off. These results indicate that combining global\u2013local representation learning with targeted noise suppression is effective for practical real-time sonar detection.<\/jats:p>","DOI":"10.3390\/a19040281","type":"journal-article","created":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T02:06:21Z","timestamp":1775441181000},"page":"281","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["T2C-DETR: A Transformer + Convolution Dual-Channel Backbone Network for Underwater Sonar Image Object Detection"],"prefix":"10.3390","volume":"19","author":[{"given":"Xiaobing","family":"Wu","sequence":"first","affiliation":[{"name":"College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4415-5752","authenticated-orcid":false,"given":"Panlong","family":"Tan","sequence":"additional","affiliation":[{"name":"Future Technology Center, Haihe Lab of ITAI, Tianjin 300459, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoyu","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Nankai University, Tianjin 300350, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Nankai University, Tianjin 300350, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"108086","DOI":"10.1016\/j.measurement.2020.108086","article-title":"Intelligent monitoring and diagnostics using a novel integrated model based on deep learning and multi-sensor feature fusion","volume":"165","author":"Xu","year":"2020","journal-title":"Measurement"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1109\/JPETS.2018.2881429","article-title":"Intelligent monitoring and inspection of power line components powered by UAVs and deep learning","volume":"6","author":"Nguyen","year":"2019","journal-title":"IEEE Power Energy Technol. 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