{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T18:22:36Z","timestamp":1791397356225,"version":"4.3.3"},"reference-count":83,"publisher":"Public Library of Science (PLoS)","issue":"10","license":[{"start":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T00:00:00Z","timestamp":1791331200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Islamic University of Technology","award":["REASP\/IUT-RSG\/2022\/OL\/07\/011"],"award-info":[{"award-number":["REASP\/IUT-RSG\/2022\/OL\/07\/011"]}],"id":[{"id":"https:\/\/ror.org\/057gnqw22","id-type":"ROR","asserted-by":"crossref"}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"\n Automatic Traffic Sign Recognition is paramount in modern transportation systems. However, curating large-scale datasets for diverse traffic sign detection remains impractical. In this context, we present FUSED-Net, a novel approach that enhances Few-Shot Object Detection (FSOD) for traffic signs using limited data. FUSED-Net integrates\n F<\/jats:bold>\n aster RCNN with\n U<\/jats:bold>\n nfrozen Parameters, Pseudo-\n S<\/jats:bold>\n upport Sets,\n E<\/jats:bold>\n mbedding Normalization, and\n D<\/jats:bold>\n omain Adaptation to improve detection accuracy. Unlike conventional methods, FUSED-Net keeps all parameters unfrozen during training, enabling it to learn effectively from limited samples. A Pseudo-Support Set is generated through data augmentation, enhancing performance by compensating for the scarcity of target domain data. Embedding Normalization reduces intra-class variance, standardizing feature representations. Domain Adaptation, achieved by pre-training on a diverse traffic sign dataset, improves model generalization. Experimental results on the BDTSD dataset demonstrate that FUSED-Net achieves\n \n \n \n \n \n 2.4<\/mml:mn>\n \u00d7<\/mml:mo>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>\n ,\n \n \n \n \n \n 2.2<\/mml:mn>\n \u00d7<\/mml:mo>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>\n ,\n \n \n \n \n \n 1.5<\/mml:mn>\n \u00d7<\/mml:mo>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>\n , and\n \n \n \n \n \n 1.3<\/mml:mn>\n \u00d7<\/mml:mo>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>\n improvements in mAP under 1-shot, 3-shot, 5-shot, and 10-shot scenarios, respectively, compared to state-of-the-art FSOD models. Additionally, FUSED-Net achieves superior performance on the cross-domain FSOD benchmark across multiple settings. The source code and the URLs to download the datasets are available at\n https:\/\/github.com\/180041123-Atiq\/FUSED-Net<\/jats:ext-link>\n .\n <\/jats:p>","DOI":"10.1371\/journal.pone.0359293","type":"journal-article","created":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T17:46:46Z","timestamp":1791395206000},"page":"e0359293","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["FUSED-Net: Detecting traffic signs with limited data"],"prefix":"10.1371","volume":"21","author":[{"given":"Md. Atiqur","family":"Rahman","sequence":"first","affiliation":[{"name":"Islamic University of Technology, Board Bazar","place":["Gazipur, Dhaka, Bangladesh"],"department":["Department of Computer Science and Engineering"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nahian Ibn","family":"Asad","sequence":"additional","affiliation":[{"name":"Islamic University of Technology, Board Bazar","place":["Gazipur, Dhaka, Bangladesh"],"department":["Department of Computer Science and Engineering"]},{"name":"TigerIT Bangladesh Ltd.","place":["Banani, Dhaka, Bangladesh"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Md. 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