{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,29]],"date-time":"2026-08-29T05:54:50Z","timestamp":1787982890793,"version":"build-2784847793"},"reference-count":53,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T00:00:00Z","timestamp":1778025600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T00:00:00Z","timestamp":1782777600000},"content-version":"vor","delay-in-days":55,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Comput Intell Syst"],"DOI":"10.1007\/s44196-026-01350-8","type":"journal-article","created":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T13:47:59Z","timestamp":1778075279000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Enhanced Detection of Small Low-Resolution Targets on Water Surfaces Via Hybrid Feature Enhancement and Adaptive IoU"],"prefix":"10.1007","volume":"19","author":[{"given":"Tianliang","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xianlai","family":"Cao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xv","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinkai","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Wan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiubin","family":"Dai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,5,6]]},"reference":[{"issue":"33","key":"1350_CR1","doi-asserted-by":"publisher","first-page":"23729","DOI":"10.1007\/s11042-020-08976-6","volume":"79","author":"Y Xiao","year":"2020","unstructured":"Xiao, Y., Tian, Z., Jiachen, Y., Zhang, Y., Liu, S., Shaoyi, D., Lan, X.: A review of object detection based on deep learning. multimed. Tools and Applications 79(33), 23729\u201323791 (2020). https:\/\/doi.org\/10.1007\/s11042-020-08976-6","journal-title":"multimed. Tools and Applications"},{"issue":"2","key":"1350_CR2","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1049\/iet-cvi.2018.5651","volume":"14","author":"J Wang","year":"2020","unstructured":"Wang, J., Haifeng, H., Xinlong, L.: Adn for object detection. IET Comput. Vision 14(2), 65\u201372 (2020). https:\/\/doi.org\/10.1049\/iet-cvi.2018.5651","journal-title":"IET Comput. Vision"},{"issue":"5","key":"1350_CR3","doi-asserted-by":"publisher","first-page":"565","DOI":"10.1049\/cvi2.12182","volume":"17","author":"H Gong","year":"2023","unstructured":"Gong, H., Li, Y., Dong, J.: A dual-balanced network for long-tail distribution object detection. IET Comput. Vision 17(5), 565\u2013575 (2023). https:\/\/doi.org\/10.1049\/cvi2.12182","journal-title":"IET Comput. Vision"},{"issue":"1","key":"1350_CR4","doi-asserted-by":"publisher","DOI":"10.1049\/cvi2.70028","volume":"19","author":"J Cao","year":"2025","unstructured":"Cao, J., Peng, B., Gao, M., Hao, H., Li, X., Mou, H.: Object detection based on cnn and vision-transformer: A survey. IET Comput. Vision 19(1), e70028 (2025). https:\/\/doi.org\/10.1049\/cvi2.70028","journal-title":"IET Comput. Vision"},{"issue":"7","key":"1350_CR5","doi-asserted-by":"publisher","first-page":"1369","DOI":"10.16383\/j.aas.c220115","volume":"49","author":"L Xiao-Bo","year":"2023","unstructured":"Xiao-Bo, L., Xiao, X., Ling, W., Zhi-Hua, C., Xin, G., Ke-Xin, Z.: Anchor-free based object detection methods and its application progress in complex scenes. Acta Automatica Sinica 49(7), 1369\u20131392 (2023). https:\/\/doi.org\/10.16383\/j.aas.c220115","journal-title":"Acta Automatica Sinica"},{"issue":"1","key":"1350_CR6","doi-asserted-by":"publisher","DOI":"10.1049\/cvi2.70011","volume":"19","author":"H Long","year":"2025","unstructured":"Long, H., Chen, H., Mengyao, X., Zhang, C., Qian, F.: Crafting transferable adversarial examples against 3d object detection. IET Comput. Vision 19(1), e70011 (2025). https:\/\/doi.org\/10.1049\/cvi2.70011","journal-title":"IET Comput. Vision"},{"key":"1350_CR7","doi-asserted-by":"publisher","unstructured":"Liang, F., Zhou, Y., Chen, X., Liu, F., Zhang, C., Wu, X.: Review of target detection technology based on deep learning. In Proceedings of the 5th international conference on control engineering and artificial intelligence, pages 132\u2013135 (2021). https:\/\/doi.org\/10.1145\/3448218.3448234","DOI":"10.1145\/3448218.3448234"},{"key":"1350_CR8","doi-asserted-by":"publisher","unstructured":"Yu, J., Jiang, Y., Wang, Z., Cao, Z., Huang, T.: Unitbox: An advanced object detection network. In Proceedings of the 24th ACM international conference on Multimedia, pages 516\u2013520, (2016). https:\/\/doi.org\/10.1145\/2964284.2967274","DOI":"10.1145\/2964284.2967274"},{"key":"1350_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.imavis.2020.103910","volume":"97","author":"K Tong","year":"2020","unstructured":"Tong, K., Yiquan, W., Zhou, F.: Recent advances in small object detection based on deep learning: A review. Image Vis. Comput. 97, 103910 (2020). https:\/\/doi.org\/10.1016\/j.imavis.2020.103910","journal-title":"Image Vis. Comput."},{"key":"1350_CR10","doi-asserted-by":"publisher","unstructured":"Lin, T-Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2117\u20132125, (2017). https:\/\/doi.org\/10.1109\/cvpr.2017.106","DOI":"10.1109\/cvpr.2017.106"},{"key":"1350_CR11","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr.2016.91","author":"J Redmon","year":"2016","unstructured":"Redmon, J.: You only look once: Unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition (2016). https:\/\/doi.org\/10.1109\/cvpr.2016.91","journal-title":"In Proceedings of the IEEE conference on computer vision and pattern recognition"},{"key":"1350_CR12","doi-asserted-by":"publisher","unstructured":"Redmon, J.: Yolov3: An incremental improvement. (2018). https:\/\/doi.org\/10.48550\/arxiv.1804.02767. arXiv preprint arXiv:1804.02767","DOI":"10.48550\/arxiv.1804.02767"},{"key":"1350_CR13","doi-asserted-by":"publisher","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C-Y., Berg, A C.: Ssd: Single shot multibox detector. In Computer Vision\u2013ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11\u201314, 2016, Proceedings, Part I 14, pages 21\u201337. Springer, (2016). https:\/\/doi.org\/10.1007\/978-3-319-46448-0_2","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"1350_CR14","doi-asserted-by":"publisher","unstructured":"Girshick, R.: Fast r-cnn. In Proceedings of the IEEE international conference on computer vision, pages 1440\u20131448 (2015). https:\/\/doi.org\/10.1109\/iccv.2015.169","DOI":"10.1109\/iccv.2015.169"},{"issue":"6","key":"1350_CR15","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","volume":"39","author":"S Ren","year":"2016","unstructured":"Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137\u20131149 (2016). https:\/\/doi.org\/10.1109\/TPAMI.2016.2577031","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1350_CR16","doi-asserted-by":"publisher","unstructured":"Cai, Z., Vasconcelos, N.: Cascade r-cnn: Delving into high quality object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 6154\u20136162 (2018) . https:\/\/doi.org\/10.1109\/CVPR.2018.00644","DOI":"10.1109\/CVPR.2018.00644"},{"key":"1350_CR17","doi-asserted-by":"publisher","unstructured":"Yu, X., Gong, Y., Jiang, N., Ye, Q., Han, Z.: Scale match for tiny person detection. In Proceedings of the IEEE\/CVF winter conference on applications of computer vision, pages 1257\u20131265 (2020). https:\/\/doi.org\/10.1109\/wacv45572.2020.9093394","DOI":"10.1109\/wacv45572.2020.9093394"},{"issue":"4","key":"1350_CR18","doi-asserted-by":"publisher","DOI":"10.3390\/s23041865","volume":"23","author":"A Betti","year":"2023","unstructured":"Betti, A., Tucci, M.: Yolo-s: A lightweight and accurate yolo-like network for small target detection in aerial imagery. Sensors 23(4), 1865 (2023). https:\/\/doi.org\/10.3390\/s23041865","journal-title":"Sensors"},{"key":"1350_CR19","doi-asserted-by":"publisher","unstructured":"Wang, C-Y., Bochkovskiy, A., Mark Liao, H-Y.: Yolov7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pages 7464\u20137475 (2023). https:\/\/doi.org\/10.1109\/cvpr52729.2023.00721","DOI":"10.1109\/cvpr52729.2023.00721"},{"key":"1350_CR20","doi-asserted-by":"publisher","unstructured":"Liu, S., Qi, L., Qin, H., Shi, J., Jia, J.: Path aggregation network for instance segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 8759\u20138768 (2018). https:\/\/doi.org\/10.1109\/cvpr.2018.00913","DOI":"10.1109\/cvpr.2018.00913"},{"key":"1350_CR21","doi-asserted-by":"publisher","unstructured":"Xu, S., Wang, X., Lv, W., Chang, Q., Cui, C., Deng, K., Wang, G., Dang, Q., Wei, S., Du, Y., et al.: Pp-yoloe: An evolved version of yolo (2022) https:\/\/doi.org\/10.48550\/arXiv.2203.16250. arXiv preprint arXiv:2203.16250","DOI":"10.48550\/arXiv.2203.16250"},{"key":"1350_CR22","doi-asserted-by":"publisher","unstructured":"Xu, X., Jiang, Y., Chen, W., Huang, Y., Zhang, Y., Sun, X.: Damo-yolo: A report on real-time object detection design. (2022) https:\/\/doi.org\/10.48550\/arXiv.2211.15444. arXiv preprint arXiv:2211.15444","DOI":"10.48550\/arXiv.2211.15444"},{"key":"1350_CR23","doi-asserted-by":"publisher","unstructured":"Sun, Z., Lin, M., Sun, X., Tan, Z., Li, H., Jin, R: Mae-det: Revisiting maximum entropy principle in zero-shot nas for efficient object detection. (2021). https:\/\/doi.org\/10.48550\/arXiv.2111.13336. arXiv preprint arXiv:2111.13336","DOI":"10.48550\/arXiv.2111.13336"},{"key":"1350_CR24","doi-asserted-by":"publisher","unstructured":"Yang, Z., Guan, Q., Zhao, K., Yang, J., Xu, X., Long, H., Tang, Y.: Multi-branch auxiliary fusion yolo with re-parameterization heterogeneous convolutional for accurate object detection. In Chinese Conference on Pattern Recognition and Computer Vision (PRCV), pages 492\u2013505. Springer, (2024). https:\/\/doi.org\/10.1007\/978-981-97-8858-3_34","DOI":"10.1007\/978-981-97-8858-3_34"},{"key":"1350_CR25","doi-asserted-by":"publisher","first-page":"8205","DOI":"10.1609\/aaai.v39i8.32885","volume":"39","author":"Z Wang","year":"2025","unstructured":"Wang, Z., Li, C., Huiying, X., Zhu, X., Li, H.: Mamba yolo: A simple baseline for object detection with state space model. In Proceedings of the AAAI Conference on Artificial Intelligence 39, 8205\u20138213 (2025). https:\/\/doi.org\/10.1609\/aaai.v39i8.32885","journal-title":"In Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"1350_CR26","doi-asserted-by":"publisher","unstructured":"Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 7132\u20137141, (2018). https:\/\/doi.org\/10.1109\/tpami.2019.2913372","DOI":"10.1109\/tpami.2019.2913372"},{"key":"1350_CR27","doi-asserted-by":"publisher","unstructured":"Woo, S., Park, J., Lee, J-Y., Kweon, I S.: Cbam: Convolutional block attention module. In Proceedings of the European conference on computer vision (ECCV), pages 3\u201319 (2018). https:\/\/doi.org\/10.1007\/978-3-030-01234-2_1","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"1350_CR28","doi-asserted-by":"publisher","unstructured":"Park, J., Woo, S., Lee, J-Y., Kweon, I S.: Bam: Bottleneck attention module. (2018). https:\/\/doi.org\/10.48550\/arXiv.1807.06514 . arXiv preprint arXiv:1807.06514","DOI":"10.48550\/arXiv.1807.06514"},{"key":"1350_CR29","doi-asserted-by":"publisher","unstructured":"Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., Hu, Q.: Eca-net: Efficient channel attention for deep convolutional neural networks. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pages 11534\u201311542, (2020). https:\/\/doi.org\/10.1109\/cvpr42600.2020.01155","DOI":"10.1109\/cvpr42600.2020.01155"},{"key":"1350_CR30","doi-asserted-by":"publisher","unstructured":"Hou, Q., Zhou, D., Feng, J.: Coordinate attention for efficient mobile network design. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pages 13713\u201313722, (2021). https:\/\/doi.org\/10.1109\/cvpr46437.2021.01350","DOI":"10.1109\/cvpr46437.2021.01350"},{"key":"1350_CR31","unstructured":"Yang, L., Zhang, R-Y., Li, L., Xie, X.: Simam: A simple, parameter-free attention module for convolutional neural networks. In International conference on machine learning, pages 11863\u201311874, (2021). https:\/\/github.com\/ZjjConan\/SimAM"},{"issue":"18","key":"1350_CR32","doi-asserted-by":"publisher","DOI":"10.3390\/electronics12183892","volume":"12","author":"Q Liu","year":"2023","unstructured":"Liu, Q., Huang, W., Duan, X., Wei, J., Tao, H., Jie, Y., Huang, J.: Dsw-yolov8n: A new underwater target detection algorithm based on improved yolov8n. Electronics 12(18), 3892 (2023). https:\/\/doi.org\/10.3390\/electronics12183892","journal-title":"Electronics"},{"key":"1350_CR33","doi-asserted-by":"publisher","unstructured":"Ouyang, D., He, S., Zhang, G., Luo, M., Guo, H., Zhan, J., Huang, Z.: Efficient multi-scale attention module with cross-spatial learning. In ICASSP 2023-2023 IEEE international conference on acoustics, speech and signal processing (ICASSP), pages 1\u20135, (2023). https:\/\/doi.org\/10.1109\/ICASSP49357.2023.10096516","DOI":"10.1109\/ICASSP49357.2023.10096516"},{"key":"1350_CR34","doi-asserted-by":"publisher","unstructured":"Huang, S., Lu, Z., Cun, X., Yu, Y., Zhou, X., Shen, X.: Deim: Detr with improved matching for fast convergence. In Proceedings of the Computer Vision and Pattern Recognition Conference, pages 15162\u201315171 (2025). https:\/\/doi.org\/10.48550\/arXiv.2412.04234","DOI":"10.48550\/arXiv.2412.04234"},{"key":"1350_CR35","doi-asserted-by":"publisher","unstructured":"Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S: End-to-end object detection with transformers. In European conference on computer vision, pages 213\u2013229, (2020). https:\/\/doi.org\/10.1007\/978-3-030-58452-8_13","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"1350_CR36","doi-asserted-by":"publisher","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. (2020). https:\/\/doi.org\/10.48550\/arXiv.2010.11929. arXiv preprint arXiv:2010.11929","DOI":"10.48550\/arXiv.2010.11929"},{"key":"1350_CR37","doi-asserted-by":"publisher","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In Proceedings of the IEEE\/CVF international conference on computer vision, pages 10012\u201310022, (2021). https:\/\/doi.org\/10.48550\/arxiv.2103.14030","DOI":"10.48550\/arxiv.2103.14030"},{"key":"1350_CR38","doi-asserted-by":"publisher","unstructured":"Rezatofighi, H., Tsoi, N., Gwak, J., Sadeghian, A., Reid, I., Savarese, S.: Generalized intersection over union: A metric and a loss for bounding box regression. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pages 658\u2013666 (2019). https:\/\/doi.org\/10.1109\/CVPR.2019.00075","DOI":"10.1109\/CVPR.2019.00075"},{"key":"1350_CR39","doi-asserted-by":"publisher","first-page":"12993","DOI":"10.1609\/aaai.v34i07.6999","volume":"34","author":"Z Zheng","year":"2020","unstructured":"Zheng, Z., Wang, P., Liu, W., Li, J., Ye, R., Ren, D.: Distance-iou loss: Faster and better learning for bounding box regression. In Proceedings of the AAAI conference on artificial intelligence 34, 12993\u201313000 (2020). https:\/\/doi.org\/10.1609\/aaai.v34i07.6999","journal-title":"In Proceedings of the AAAI conference on artificial intelligence"},{"key":"1350_CR40","doi-asserted-by":"publisher","unstructured":"Gevorgyan, Z.: Siou loss: More powerful learning for bounding box regression. (2022). https:\/\/doi.org\/10.48550\/arXiv.2205.12740. arXiv preprint arXiv:2205.12740","DOI":"10.48550\/arXiv.2205.12740"},{"key":"1350_CR41","doi-asserted-by":"publisher","unstructured":"Wang, J., Xu, C., Yang, W., Yu, L.: A normalized gaussian wasserstein distance for tiny object detection. (2021). https:\/\/doi.org\/10.48550\/arXiv.2110.13389. arXiv preprint arXiv:2110.13389","DOI":"10.48550\/arXiv.2110.13389"},{"key":"1350_CR42","doi-asserted-by":"publisher","unstructured":"Lin, T-Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2117\u20132125, (2017). https:\/\/doi.org\/10.48550\/arXiv.1612.03144","DOI":"10.48550\/arXiv.1612.03144"},{"key":"1350_CR43","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2025.3538473","author":"Y Chen","year":"2025","unstructured":"Chen, Y., Yuan, X., Wang, J., Ruiqi, W., Li, X., Hou, Q., Cheng, M.-M.: Yolo-ms: Rethinking multi-scale representation learning for real-time object detection. IEEE Trans. Pattern Anal. Mach. Intell. (2025). https:\/\/doi.org\/10.1109\/TPAMI.2025.3538473","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1350_CR44","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.125442","volume":"260","author":"P Wang","year":"2025","unstructured":"Wang, P., Luo, Y., Zhu, Z.: Fdi-yolo: Feature disentanglement and interaction network based on yolo for sar object detection. Expert Systems with Applications 260, 125442 (2025). https:\/\/doi.org\/10.1016\/j.eswa.2024.125442","journal-title":"Expert Systems with Applications"},{"key":"1350_CR45","doi-asserted-by":"publisher","first-page":"6896","DOI":"10.1016\/j.eswa.2024.125442","volume":"39","author":"Z Shi","year":"2025","unstructured":"Shi, Z., Jing, H., Ren, J., Ye, H., Yuan, X., Ouyang, Y., He, J., Ji, B., Guo, J.: Hs-fpn: High frequency and spatial perception fpn for tiny object detection. In Proceedings of the AAAI Conference on Artificial Intelligence 39, 6896\u20136904 (2025). https:\/\/doi.org\/10.1016\/j.eswa.2024.125442","journal-title":"In Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"1350_CR46","doi-asserted-by":"publisher","DOI":"10.3389\/fnbot.2023.1210470","volume":"17","author":"Q Zhu","year":"2023","unstructured":"Zhu, Q., Ma, K., Wang, Z., Shi, P.: Yolov7-csaw for maritime target detection. Front. Neurorobot. 17, 1210470 (2023). https:\/\/doi.org\/10.3389\/fnbot.2023.1210470","journal-title":"Front. Neurorobot."},{"key":"1350_CR47","doi-asserted-by":"publisher","DOI":"10.3389\/fmars.2022.1058401","volume":"9","author":"J Zhang","year":"2023","unstructured":"Zhang, J., Jin, J., Ma, Y., Ren, P.: Lightweight object detection algorithm based on yolov5 for unmanned surface vehicles. Front. Mar. Sci. 9, 1058401 (2023). https:\/\/doi.org\/10.3389\/fmars.2022.1058401","journal-title":"Front. Mar. Sci."},{"key":"1350_CR48","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2024.3485463","author":"Q Ge","year":"2024","unstructured":"Ge, Q., Da, W., Wang, M.: Marfpnet: Multi-attention and adaptive reparameterized feature pyramid network for small target detection on water surfaces. IEEE Trans. Instrum. Meas. (2024). https:\/\/doi.org\/10.1109\/TIM.2024.3485463","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"1350_CR49","doi-asserted-by":"publisher","unstructured":"Lin, H., Deng, H., Du, H., Liu, Y., Xu, J.: Low visibility underwater biological target detection based on the improved yolov5s: H. lin et al. The Visual Computer, pages 1\u201314 (2025). https:\/\/doi.org\/10.1007\/s00371-025-04042-0","DOI":"10.1007\/s00371-025-04042-0"},{"key":"1350_CR50","doi-asserted-by":"publisher","unstructured":"Cheng, Y., Zhu, J., Jiang, M., Fu, J., Pang, C., Wang, P., Sankaran, K., Onabola, O., Liu, Y., Liu, D., et al.: Flow: A dataset and benchmark for floating waste detection in inland waters. In Proceedings of the IEEE\/CVF international conference on computer vision, pages 10953\u201310962, (2021). https:\/\/doi.org\/10.1109\/iccv48922.2021.01077","DOI":"10.1109\/iccv48922.2021.01077"},{"key":"1350_CR51","doi-asserted-by":"publisher","DOI":"10.3389\/fnbot.2021.723336","volume":"15","author":"Z Zhou","year":"2021","unstructured":"Zhou, Z., Sun, J., Yu, J., Liu, K., Duan, J., Chen, L., Philip Chen, C.L.: An image-based benchmark dataset and a novel object detector for water surface object detection. Frontiers in Neurorobotics 15, 723336 (2021). https:\/\/doi.org\/10.3389\/fnbot.2021.723336","journal-title":"Frontiers in Neurorobotics"},{"key":"1350_CR52","doi-asserted-by":"publisher","unstructured":"Jocher, G., Chaurasia, A., Stoken, A., Borovec, J., Kwon, Y., Michael, K., Fang, J., Yifu, Z., Wong, C., Montes, D., et al.: ultralytics\/yolov5: v7. 0-yolov5 sota realtime instance segmentation. Zenodo (2022). https:\/\/doi.org\/10.5281\/zenodo.3908559","DOI":"10.5281\/zenodo.3908559"},{"key":"1350_CR53","doi-asserted-by":"publisher","unstructured":"Ge, Z.: Yolox: Exceeding yolo series in 2021. (2021). https:\/\/doi.org\/10.48550\/arXiv.2107.08430 . arXiv preprint arXiv:2107.08430","DOI":"10.48550\/arXiv.2107.08430"}],"container-title":["International Journal of Computational Intelligence Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44196-026-01350-8","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44196-026-01350-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44196-026-01350-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,29]],"date-time":"2026-06-29T22:35:51Z","timestamp":1782772551000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44196-026-01350-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,6]]},"references-count":53,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["1350"],"URL":"https:\/\/doi.org\/10.1007\/s44196-026-01350-8","relation":{},"ISSN":["1875-6883"],"issn-type":[{"value":"1875-6883","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,6]]},"assertion":[{"value":"29 October 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 April 2026","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 April 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 May 2026","order":4,"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 conflicts of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of Interest"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}}],"article-number":"245"}}