{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,30]],"date-time":"2025-12-30T18:34:48Z","timestamp":1767119688777,"version":"3.48.0"},"reference-count":26,"publisher":"Public Library of Science (PLoS)","issue":"12","license":[{"start":{"date-parts":[[2025,12,30]],"date-time":"2025-12-30T00:00:00Z","timestamp":1767052800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>This paper proposes a few-shot object detection algorithm based on FSCE, tailored for multi-scenario driving environments. Unlike existing methods that focus on single scenarios, our approach addresses challenges of cross-scenario heterogeneity and overfitting in low-data regimes. We enhance feature representation through a multi-scale feature module that integrates local and contextual information, and replace the traditional Softmax with a cosine Softmax classifier to reduce intra-class variance via L2 normalization and angular margin constraints. This work is the first to apply few-shot detection to both nighttime infrared and daytime visible-light driving scenarios. Experiments on FLIR and BDD100K demonstrate superior generalization and accuracy over existing methods. Future work will explore reducing model complexity while maintaining performance.<\/jats:p>","DOI":"10.1371\/journal.pone.0337541","type":"journal-article","created":{"date-parts":[[2025,12,30]],"date-time":"2025-12-30T18:33:05Z","timestamp":1767119585000},"page":"e0337541","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["A novel few-shot object detection framework for multi-scene driving based on contrastive proposal encoding"],"prefix":"10.1371","volume":"20","author":[{"given":"Yalei","family":"Dong","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Xiao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9870-2419","authenticated-orcid":true,"given":"Fengchen","family":"Wei","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"340","published-online":{"date-parts":[[2025,12,30]]},"reference":[{"key":"pone.0337541.ref001","doi-asserted-by":"crossref","unstructured":"Wei F, Wang W. OFVO: a visual odometry designed for motion trajectory estimation of autonomous vehicles. In: 2024 4th International Conference on Robotics, Automation and Artificial Intelligence (RAAI). 2024. p. 119\u201323. https:\/\/doi.org\/10.1109\/raai64504.2024.10949531","DOI":"10.1109\/RAAI64504.2024.10949531"},{"key":"pone.0337541.ref002","doi-asserted-by":"crossref","unstructured":"Wei F, Wang W. GS-YoloNet: a lightweight network for detection, tracking, and distance estimation on highways. In: 2024 IEEE 99th Vehicular Technology Conference (VTC2024-Spring). 2024. p. 1\u20136.","DOI":"10.1109\/VTC2024-Spring62846.2024.10683547"},{"key":"pone.0337541.ref003","doi-asserted-by":"crossref","unstructured":"Wei F, Wang W. A real-time large animal detection lightweight network for autonomous driving on highways. In: 2023 7th CAA International Conference on Vehicular Control and Intelligence (CVCI). 2023. p. 1\u20136. https:\/\/doi.org\/10.1109\/cvci59596.2023.10397380","DOI":"10.1109\/CVCI59596.2023.10397380"},{"issue":"1","key":"pone.0337541.ref004","doi-asserted-by":"crossref","first-page":"6459","DOI":"10.1038\/s41598-025-90743-4","article-title":"SCCA-YOLO: a spatial and channel collaborative attention enhanced YOLO network for highway autonomous driving perception system","volume":"15","author":"F Wei","year":"2025","journal-title":"Sci Rep."},{"key":"pone.0337541.ref005","doi-asserted-by":"crossref","unstructured":"Wei F, Wang W. A method for designing the perception module of autonomous vehicles using stereo depth and semantic segmentation. In: 2024 24th International Conference on Control, Automation and Systems (ICCAS). 2024. p. 1423\u20138. https:\/\/doi.org\/10.23919\/iccas63016.2024.10773283","DOI":"10.23919\/ICCAS63016.2024.10773283"},{"issue":"6","key":"pone.0337541.ref006","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: towards real-time object detection with region proposal networks","volume":"39","author":"S Ren","year":"2017","journal-title":"IEEE Trans Pattern Anal Mach Intell."},{"key":"pone.0337541.ref007","doi-asserted-by":"crossref","unstructured":"Redmon J, Divvala S, Girshick R, Farhadi A. You only look once: unified, real-time object detection. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2016. p. 779\u201388.","DOI":"10.1109\/CVPR.2016.91"},{"key":"pone.0337541.ref008","doi-asserted-by":"crossref","unstructured":"Redmon J, Farhadi A. YOLO9000: better, faster, stronger. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2017. p. 6517\u201325. https:\/\/doi.org\/10.1109\/cvpr.2017.690","DOI":"10.1109\/CVPR.2017.690"},{"key":"pone.0337541.ref009","article-title":"YOLOv3: an incremental improvement","author":"J Redmon","year":"2018","journal-title":"arXiv preprint"},{"key":"pone.0337541.ref010","unstructured":"Jocher G. YOLOv5 by ultralytics. 2020. https:\/\/github.com\/ultralytics\/yolov5"},{"key":"pone.0337541.ref011","unstructured":"Jocher G, Chaurasia A, Qiu J. Ultralytics YOLOv8. 2023. https:\/\/github.com\/ultralytics\/ultralytics"},{"key":"pone.0337541.ref012","article-title":"YOLOv10: real-time end-to-end object detection","author":"A Wang","year":"2024","journal-title":"arXiv preprint"},{"key":"pone.0337541.ref013","doi-asserted-by":"crossref","unstructured":"Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu C-Y, et al. SSD: Single Shot MultiBox Detector. Lecture Notes in Computer Science. Springer; 2016. p. 21\u201337. https:\/\/doi.org\/10.1007\/978-3-319-46448-0_2","DOI":"10.1007\/978-3-319-46448-0_2"},{"issue":"2","key":"pone.0337541.ref014","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1109\/TPAMI.2018.2858826","article-title":"Focal loss for dense object detection","volume":"42","author":"T-Y Lin","year":"2020","journal-title":"IEEE Trans Pattern Anal Mach Intell."},{"key":"pone.0337541.ref015","unstructured":"Han G, Huang S, Ma J, He Y, Chang SF. Meta faster R-CNN: towards accurate few-shot object detection with attentive feature alignment. 2021. https:\/\/doi.org\/abs\/2104.07719"},{"key":"pone.0337541.ref016","unstructured":"Wang X, Huang TE, Darrell T, Gonzalez JE, Yu F. Frustratingly simple few-shot object detection. In: Proceedings of the 37th International Conference on Machine Learning. ICML\u201920. JMLR.org; 2020."},{"key":"pone.0337541.ref017","doi-asserted-by":"crossref","unstructured":"Sun B, Li B, Cai S, Yuan Y, Zhang C. FSCE: few-shot object detection via contrastive proposal encoding. In: 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 2021. p. 7348\u201358. https:\/\/doi.org\/10.1109\/cvpr46437.2021.00727","DOI":"10.1109\/CVPR46437.2021.00727"},{"key":"pone.0337541.ref018","doi-asserted-by":"crossref","unstructured":"Yan X, Chen Z, Xu A, Wang X, Liang X, Lin L. Meta R-CNN: towards general solver for instance-level low-shot learning. In: 2019 IEEE\/CVF International Conference on Computer Vision (ICCV). 2019. p. 9576\u201385. https:\/\/doi.org\/10.1109\/iccv.2019.00967","DOI":"10.1109\/ICCV.2019.00967"},{"issue":"6","key":"pone.0337541.ref019","first-page":"14","article-title":"Optical instruments","volume":"45","author":"Y Tang","year":"2023","journal-title":"Journal of Optical Instruments."},{"key":"pone.0337541.ref020","doi-asserted-by":"crossref","unstructured":"Kaul P, Xie W, Zisserman A. Label, verify, correct: a simple few shot object detection method. In: 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 2022. p. 14217\u201327.","DOI":"10.1109\/CVPR52688.2022.01384"},{"key":"pone.0337541.ref021","article-title":"Objects as points","author":"X Zhou","year":"2019","journal-title":"arXiv preprint"},{"key":"pone.0337541.ref022","doi-asserted-by":"crossref","unstructured":"Kang B, Liu Z, Wang X, Yu F, Feng J, Darrell T. Few-shot object detection via feature reweighting. In: 2019 IEEE\/CVF International Conference on Computer Vision (ICCV). 2019. p. 8419\u201328.","DOI":"10.1109\/ICCV.2019.00851"},{"issue":"11","key":"pone.0337541.ref023","first-page":"12832","article-title":"Meta-DETR: image-level few-shot detection with inter-class correlation exploitation","volume":"45","author":"G Zhang","year":"2023","journal-title":"IEEE Trans Pattern Anal Mach Intell."},{"key":"pone.0337541.ref024","doi-asserted-by":"crossref","unstructured":"Qiao L, Zhao Y, Li Z, Qiu X, Wu J, Zhang C. DeFRCN: decoupled faster R-CNN for few-shot object detection. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV); 2021. p. 8681\u201390.","DOI":"10.1109\/ICCV48922.2021.00856"},{"key":"pone.0337541.ref025","doi-asserted-by":"crossref","unstructured":"Fan Z, Ma Y, Li Z, Sun J. Generalized few-shot object detection without forgetting. In: 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 2021; p. 4525\u201334.","DOI":"10.1109\/CVPR46437.2021.00450"},{"key":"pone.0337541.ref026","doi-asserted-by":"crossref","unstructured":"Yu F, Chen H, Wang X, Xian W, Chen Y, Liu F, et al. BDD100K: a diverse driving dataset for heterogeneous multitask learning. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 2020. p. 2633\u201342. https:\/\/doi.org\/10.1109\/cvpr42600.2020.00271","DOI":"10.1109\/CVPR42600.2020.00271"}],"container-title":["PLOS One"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0337541","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,30]],"date-time":"2025-12-30T18:33:15Z","timestamp":1767119595000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0337541"}},"subtitle":[],"editor":[{"given":"Jinhao","family":"Liang","sequence":"first","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2025,12,30]]},"references-count":26,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2025,12,30]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0337541","relation":{},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,30]]}}}