{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:21:33Z","timestamp":1777702893151,"version":"3.51.4"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T00:00:00Z","timestamp":1760400000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T00:00:00Z","timestamp":1760400000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Real-Time Image Proc"],"published-print":{"date-parts":[[2025,12]]},"DOI":"10.1007\/s11554-025-01778-9","type":"journal-article","created":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T17:01:55Z","timestamp":1760461315000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["LUOD-YOLO: a lightweight underwater object detection model based on dynamic feature fusion, dual path rearrangement and cross-scale integration"],"prefix":"10.1007","volume":"22","author":[{"given":"Chengze","family":"Lv","sequence":"first","affiliation":[]},{"given":"Weichao","family":"Pan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,10,14]]},"reference":[{"key":"1778_CR1","doi-asserted-by":"publisher","unstructured":"Xue, Y., Zhong, B., Jin, G., Shen, T., Tan, L., Li, N., Zheng, Y.: AVLTrack: Dynamic sparse learning for aerial vision-language tracking. IEEE Trans. Circuits Syst. Video Technol. 1\u20131 (2025) https:\/\/doi.org\/10.1109\/TCSVT.2025.3549953.","DOI":"10.1109\/TCSVT.2025.3549953."},{"key":"1778_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2023.3305728","volume":"61","author":"Y Xue","year":"2023","unstructured":"Xue, Y., et al.: SmallTrack: Wavelet pooling and graph enhanced classification for UAV small object tracking. IEEE Trans. Geosci Remote Sens 61, 1\u201315 (2023). https:\/\/doi.org\/10.1109\/TGRS.2023.3305728","journal-title":"IEEE Trans. Geosci Remote Sens"},{"issue":"11","key":"1778_CR3","doi-asserted-by":"publisher","first-page":"10845","DOI":"10.1109\/TCSVT.2024.3411301","volume":"34","author":"Y Xue","year":"2024","unstructured":"Xue, Y., et al.: Consistent representation mining for multi-drone single object tracking. IEEE Trans. Circuits Syst. Video Technol. 34(11), 10845\u201310859 (2024). https:\/\/doi.org\/10.1109\/TCSVT.2024.3411301","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"1778_CR4","doi-asserted-by":"publisher","first-page":"3097","DOI":"10.1007\/s11760-023-02973-6","volume":"18","author":"K Singh","year":"2024","unstructured":"Singh, K., Parihar, A.S.: Bff: bi-stream feature fusion for object detection in hazy environment. SIViP 18, 3097\u20133107 (2024). https:\/\/doi.org\/10.1007\/s11760-023-02973-6","journal-title":"SIViP"},{"issue":"4","key":"1778_CR5","doi-asserted-by":"publisher","first-page":"603","DOI":"10.1139\/cjfas-2022-0270","volume":"80","author":"L Ou","year":"2023","unstructured":"Ou, L., Liu, B., Chen, X.: Automatic classification of the phenotype textures of three Thunnus species based on the machine-learning SVM algorithm. Can. J. Fish. Aquat. Sci. 80(4), 603\u2013612 (2023). https:\/\/doi.org\/10.1139\/cjfas-2022-0270","journal-title":"Can. J. Fish. Aquat. Sci."},{"issue":"2","key":"1778_CR6","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1007\/s10452-023-09955-2","volume":"58","author":"RM Connolly","year":"2024","unstructured":"Connolly, R.M., et al.: Fish detection in acoustic camera imagery using deep learning. Aquat. Ecol. 58(2), 187\u2013201 (2024). https:\/\/doi.org\/10.1007\/s10452-023-09955-2","journal-title":"Aquat. Ecol."},{"key":"1778_CR7","unstructured":"Pan, W., Wang, X., Huan, W.: EFA-YOLO: An efficient feature attention model for fire and flame detection. (2024) arXiv preprint arXiv:2409.12635"},{"key":"1778_CR8","doi-asserted-by":"publisher","unstructured":"Pan, W., Wang, X., Huan, W.: Real-time dynamic scale-aware fusion detection network: take road damage detection as an example. J. Real-Time Image Process. 22(55) (2025). https:\/\/doi.org\/10.1007\/s11554-025-01634-w","DOI":"10.1007\/s11554-025-01634-w"},{"issue":"3","key":"1778_CR9","doi-asserted-by":"publisher","first-page":"1470","DOI":"10.3390\/app15031470","volume":"15","author":"W Pan","year":"2025","unstructured":"Pan, W., Lei, J., Wang, X., Lv, C., Wang, G., Li, C.: Daponet: a dual attention and partially overparameterized network for real-time road damage detection. Appl. Sci. 15(3), 1470 (2025). https:\/\/doi.org\/10.3390\/app15031470","journal-title":"Appl. Sci."},{"key":"1778_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.ecoinf.2024.102758","volume":"82","author":"J Feng","year":"2024","unstructured":"Feng, J., Jin, T.: CEH-YOLO: A composite enhanced YOLO-based model for underwater object detection. Ecol. Inform. 82, 102758 (2024). https:\/\/doi.org\/10.1016\/j.ecoinf.2024.102758","journal-title":"Ecol. Inform."},{"issue":"8","key":"1778_CR11","doi-asserted-by":"publisher","first-page":"1669","DOI":"10.3390\/sym14081669","volume":"14","author":"S Zhao","year":"2022","unstructured":"Zhao, S., Zheng, J., Sun, S., Zhang, L.: An improved YOLO algorithm for fast and accurate underwater object detection. Symmetry 14(8), 1669 (2022). https:\/\/doi.org\/10.3390\/sym14081669","journal-title":"Symmetry"},{"key":"1778_CR12","doi-asserted-by":"publisher","unstructured":"J. Hu, Shen, L., Sun, G.: Squeeze-and-Excitation Networks, 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 2018, pp. 7132-7141, https:\/\/doi.org\/10.1109\/CVPR.2018.00745.","DOI":"10.1109\/CVPR.2018.00745."},{"key":"1778_CR13","doi-asserted-by":"publisher","unstructured":"Woo, S., Park, J., Lee, JY., Kweon, I.S.: CBAM: Convolutional Block Attention Module. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds) Computer Vision \u2013 ECCV 2018. ECCV 2018. Lecture Notes in Computer Science, vol 11211. Springer, Cham. (2018) https:\/\/doi.org\/10.1007\/978-3-030-01234-2_1","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"1778_CR14","doi-asserted-by":"publisher","unstructured":"Rao, Y., He, D., Wang, X., Li, J.: Global relation-aware attention network for image-text matching. In: Proceedings of the 2021 International Conference on Multimedia Retrieval (ICMR \u201921) (pp. 231\u2013239). Association for Computing Machinery, New York, NY, USA. (2021) https:\/\/doi.org\/10.1145\/3460426.3463615","DOI":"10.1145\/3460426.3463615"},{"key":"1778_CR15","doi-asserted-by":"publisher","unstructured":"Wang, Q., Xiao, B., Hou, S., Li, J., Jiang, C., Peng, X.: ECA-Net: Efficient channel attention for deep convolutional neural networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 11534\u201311542). (2020) https:\/\/doi.org\/10.1109\/CVPR42600.2020.01155","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"1778_CR16","doi-asserted-by":"publisher","unstructured":"Wang, J., Yu, N.: UTD-Yolov5: A real-time underwater objects detection method based on attention improved YOLOv5. Neurocomputing. (2025) https:\/\/doi.org\/10.1016\/j.neucom.2025.04.002","DOI":"10.1016\/j.neucom.2025.04.002"},{"issue":"3","key":"1778_CR17","doi-asserted-by":"publisher","first-page":"345","DOI":"10.3390\/jmse11030677","volume":"50","author":"K Liu","year":"2024","unstructured":"Liu, K., Sun, Q., Sun, D., Yang, M., Wang, N.: Underwater object detection based on improved YOLOv7. Marine Technol. Geomat. J. 50(3), 345\u2013358 (2024). https:\/\/doi.org\/10.3390\/jmse11030677","journal-title":"Marine Technol. Geomat. J."},{"key":"1778_CR18","doi-asserted-by":"crossref","unstructured":"Li, C., Liu, W., Gong, G., Ding, X., Zhong, X.: SU-YOLO: Spiking neural network for efficient underwater object detection. (2025) arXiv preprint. arXiv:2503.24389","DOI":"10.1016\/j.neucom.2025.130310"},{"issue":"2","key":"1778_CR19","doi-asserted-by":"publisher","first-page":"57","DOI":"10.12677\/csa.2025.152033","volume":"15","author":"LN Chang","year":"2025","unstructured":"Chang, L.N., Yuan, C.M., Yang, Q.Y.: YOLO-Vortex: Underwater object detection model based on vortex aggregation network. Comput. Sci. Appl. 15(2), 57\u201370 (2025). https:\/\/doi.org\/10.12677\/csa.2025.152033","journal-title":"Comput. Sci. Appl."},{"issue":"6","key":"1778_CR20","doi-asserted-by":"publisher","first-page":"74","DOI":"10.3969\/j.issn.1007-9580.2023.06.009","volume":"50","author":"H Yuan","year":"2023","unstructured":"Yuan, H., Shi, J.: Study on Tuna automatic detection and counting based on improved YOLOv7-tiny and dynamic detection gate. Fishery Moderniz. 50(6), 74\u201387 (2023). https:\/\/doi.org\/10.3969\/j.issn.1007-9580.2023.06.009","journal-title":"Fishery Moderniz."},{"key":"1778_CR21","doi-asserted-by":"publisher","unstructured":"Guo, A., Sun, K., Zhang, Z.: A lightweight YOLOv8 integrating FasterNet for real-time underwater object detection. J. Real-Time Image Process. 1\u201315. (2024) https:\/\/doi.org\/10.1007\/s11554-024-01431-x.","DOI":"10.1007\/s11554-024-01431-x."},{"key":"1778_CR22","doi-asserted-by":"publisher","unstructured":"Wu, C., Sun, Y., Wang, T., Liu, Y.: Underwater trash detection algorithm based on improved YOLOv5s. J. Real-Time Image Process. 19(4). (2022) https:\/\/doi.org\/10.1007\/s11554-022-01232-0.","DOI":"10.1007\/s11554-022-01232-0."},{"key":"1778_CR23","doi-asserted-by":"publisher","first-page":"144","DOI":"10.1007\/s11554-025-01726-7","volume":"22","author":"H Chen","year":"2025","unstructured":"Chen, H., Tang, X., Xiang, Q., et al.: Dsh-yolo: a lightweight framework with enhanced multi-scale features fusion for water surface object detection. J. Real-Time Image Process. 22, 144 (2025). https:\/\/doi.org\/10.1007\/s11554-025-01726-7","journal-title":"J. Real-Time Image Process."},{"key":"1778_CR24","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1007\/s11554-025-01682-2","volume":"22","author":"JL Mela","year":"2025","unstructured":"Mela, J.L., S\u00e1nchez, C.G.: Yolo-based power-efficient object detection on edge devices for usvs. J. Real-Time Image Process. 22, 108 (2025). https:\/\/doi.org\/10.1007\/s11554-025-01682-2","journal-title":"J. Real-Time Image Process."},{"key":"1778_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.jvcir.2024.104079","volume":"99","author":"K Singh","year":"2024","unstructured":"Singh, K., Parihar, A.S.: MRN-LOD: Multi-exposure refinement network for low-light object detection. J. Vis. Commun. Image Represent. 99, 104079 (2024). https:\/\/doi.org\/10.1016\/j.jvcir.2024.104079","journal-title":"J. Vis. Commun. Image Represent."},{"key":"1778_CR26","doi-asserted-by":"publisher","unstructured":"Zhao, Y., Lv, W., Xu, S., Wei, J., Wang, G., Dang, Q., Liu, Y., Chen, J.: DETRs beat YOLOs on real-time object detection. 2024 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA, 2024, pp. 16965-16974, (2024) https:\/\/doi.org\/10.1109\/CVPR52733.2024.01605.","DOI":"10.1109\/CVPR52733.2024.01605."},{"key":"1778_CR27","unstructured":"Jocher, G., Qiu, J., Chaurasia, A.: Ultralytics YOLO (Version 8.0.0) [Computer software], (2023) [Online]. Available: https:\/\/github.com\/ultralytics\/ultralytics"},{"key":"1778_CR28","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1007\/978-3-031-61069-1","volume":"693","author":"Y Ouassine","year":"2024","unstructured":"Ouassine, Y., Zahir, J., Conruyt, N., Kayal, M., Martin, P.A., Chenin, E., Bigot, L., Vignes Lebbe, R.: Automatic coral morphotypes detection with yolo: a deep learning approach for efficient and accurate coral reef monitoring. IFIP Adv. Inform. Commun. Technol. 693, 177\u2013188 (2024). https:\/\/doi.org\/10.1007\/978-3-031-61069-1","journal-title":"IFIP Adv. Inform. Commun. Technol."},{"key":"1778_CR29","doi-asserted-by":"publisher","unstructured":"Chen, L., Liu, Z., Tong, L., Jiang, Z., Wang, S., Dong, J., Zhou, H.: Underwater object detection using invert multi-class adaboost with deep learning. 2020 International Joint Conference on Neural Networks (IJCNN), Glasgow, UK, 2020, pp. 1-8, (2020) https:\/\/doi.org\/10.1109\/IJCNN48605.2020.9207506.","DOI":"10.1109\/IJCNN48605.2020.9207506."},{"key":"1778_CR30","doi-asserted-by":"publisher","first-page":"3196","DOI":"10.3390\/app14083196","volume":"14","author":"H Wang","year":"2024","unstructured":"Wang, H., Zhang, P., You, M., You, X.: A method for underwater biological detection based on improved yoloxs. Appl. Sci. 14, 3196 (2024). https:\/\/doi.org\/10.3390\/app14083196","journal-title":"Appl. Sci."},{"key":"1778_CR31","doi-asserted-by":"publisher","unstructured":"Jiang, L., Wang, Y., Jia, Q., Xu, S.: Underwater Species Detection using Channel Sharpening Attention [Data set]. MM \u201921: ACM Multimedia Conference. (2021) https:\/\/doi.org\/10.1145\/3474085.3475563","DOI":"10.1145\/3474085.3475563"},{"key":"1778_CR32","doi-asserted-by":"crossref","unstructured":"Liu, C., Li, H., Wang, S., Zhu, M., Wang, D., Fan, X., Wang, Z.: DUO: A Dataset and Benchmark of Underwater Object Detection for Robot Picking. DUT-RU International School of Information Science & Engineering, Dalian University of Technology. (2021) https:\/\/github.com\/chongweiliu","DOI":"10.1109\/ICMEW53276.2021.9455997"},{"key":"1778_CR33","doi-asserted-by":"publisher","unstructured":"Jocher, G.: YOLOv5 by Ultralytics, version 7.0, May 2020. [Online]. Available: https:\/\/github.com\/ultralytics\/yolov5. https:\/\/doi.org\/10.5281\/zenodo.3908559","DOI":"10.5281\/zenodo.3908559"},{"key":"1778_CR34","doi-asserted-by":"publisher","unstructured":"Wang, C.-Y. , Yeh, I.-H., Liao, H.-Y. M. : \"YOLOv9: Learning what you want to learn using programmable gradient information,\" in Proc. Eur. Conf. Computer Vision (ECCV), vol. 15089, Springer, Cham, 2025, pp. 1\u201316. https:\/\/doi.org\/10.1007\/978-3-031-72751-1\\_1doi: .","DOI":"10.1007\/978-3-031-72751-1\\_1"},{"key":"1778_CR35","doi-asserted-by":"crossref","unstructured":"Wang, A., Chen, H.,  Liu, L., Chen, K., Lin,  Z., Han, J., Ding, G.: \"YOLOv10: Real-time end-to-end object detection,\" in Proc. Advances in Neural Information Processing Systems (NeurIPS), vol. 37, 2024, pp. 107984\u2013108011. [Online]. Available: https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2024\/file\/c34ddd05eb089991f06f3c5dc36836e0-Paper-Conference","DOI":"10.52202\/079017-3429"},{"key":"1778_CR36","unstructured":"G. Jocher, J. Qiu, and A. Chaurasia, Ultralytics YOLO (Version 8.3.208) [Computer software], 2025. [Online]. Available: https:\/\/github.com\/ultralytics\/ultralytics"},{"key":"1778_CR37","unstructured":"Y. Tian, Q. Ye, and D. Doermann, \"YOLOv12: Attention-centric real-time object detectors,\" arXiv preprint arXiv:2502.12524, 2025."},{"key":"1778_CR38","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.108785","volume":"129","author":"G Li","year":"2022","unstructured":"Li, G., Fang, Q., Zha, L., Gao, X., Zheng, N.: HAM: Hybrid attention module in deep convolutional neural networks for image classification. Pattern Detection 129, 108785 (2022). https:\/\/doi.org\/10.1016\/j.patcog.2022.108785","journal-title":"Pattern Detection"},{"issue":"1169\u20131179","key":"1778_CR39","doi-asserted-by":"publisher","first-page":"2021","DOI":"10.1109\/TIP.2020.3042065","volume":"30","author":"T Wu","year":"2019","unstructured":"Wu, T., Tang, S., Zhang, R., Zhang, Y.: CGNet: A light-weight context guided network for semantic segmentation. IEEE Trans. Image Process. 30(1169\u20131179), 2021 (2019). https:\/\/doi.org\/10.1109\/TIP.2020.3042065","journal-title":"IEEE Trans. Image Process."},{"key":"1778_CR40","doi-asserted-by":"publisher","unstructured":"Liu, X., Peng, H., Zheng, N., Yang, Y., Hu, H., Yuan, Y.: EfficientViT: Memory Efficient Vision Transformer with Cascaded Group Attention. 2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, BC, Canada, 2023, pp. 14420-14430, (2023) https:\/\/doi.org\/10.1109\/CVPR52729.2023.01386.","DOI":"10.1109\/CVPR52729.2023.01386."},{"issue":"9","key":"1778_CR41","doi-asserted-by":"publisher","first-page":"1526","DOI":"10.3390\/rs1709152","volume":"17","author":"N Wang","year":"2025","unstructured":"Wang, N., Cui, Z., Lan, Y., Zhang, C., Xue, Y., Su, Y., Li, A.: Large-scale hyperspectral image-projected clustering via doubly stochastic graph learning. Remote Sensing 17(9), 1526 (2025). https:\/\/doi.org\/10.3390\/rs1709152","journal-title":"Remote Sensing"}],"container-title":["Journal of Real-Time Image Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11554-025-01778-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11554-025-01778-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11554-025-01778-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,10]],"date-time":"2025-11-10T07:04:47Z","timestamp":1762758287000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11554-025-01778-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,14]]},"references-count":41,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2025,12]]}},"alternative-id":["1778"],"URL":"https:\/\/doi.org\/10.1007\/s11554-025-01778-9","relation":{},"ISSN":["1861-8200","1861-8219"],"issn-type":[{"value":"1861-8200","type":"print"},{"value":"1861-8219","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,14]]},"assertion":[{"value":"2 June 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 September 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 October 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no Conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"204"}}