{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T08:09:49Z","timestamp":1778486989267,"version":"3.51.4"},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T00:00:00Z","timestamp":1770336000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T00:00:00Z","timestamp":1770336000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62403159"],"award-info":[{"award-number":["62403159"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62403159"],"award-info":[{"award-number":["62403159"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Real-Time Image Proc"],"published-print":{"date-parts":[[2026,4]]},"DOI":"10.1007\/s11554-025-01841-5","type":"journal-article","created":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T11:43:33Z","timestamp":1770378213000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["ARD: attention-guided reweighted knowledge distillation on image-like feature representations for real-time unmanned surface vehicles detection from LiDAR point clouds"],"prefix":"10.1007","volume":"23","author":[{"given":"Jun","family":"Bai","sequence":"first","affiliation":[]},{"given":"Shiyang","family":"Li","sequence":"additional","affiliation":[]},{"given":"Guihua","family":"Xia","sequence":"additional","affiliation":[]},{"given":"Chunsheng","family":"Yang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,2,6]]},"reference":[{"key":"1841_CR1","doi-asserted-by":"crossref","unstructured":"Ahn, S., Hu, S.X., Damianou, A., Lawrence, N.D., Dai, Z.: Variational information distillation for knowledge transfer. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 9163\u20139171. IEEE\/CVF, Long Beach, CA, USA (2019)","DOI":"10.1109\/CVPR.2019.00938"},{"key":"1841_CR2","doi-asserted-by":"crossref","unstructured":"Caesar, H., Bankiti, V., Lang, A.H., Vora, S., Liong, V.E., Xu, Q., Krishnan, A., Pan, Y., Baldan, G., Beijbom, O.: nuScenes: A multimodal dataset for autonomous driving. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 11621\u201311631. IEEE\/CVF, Seattle, WA, USA (2020)","DOI":"10.1109\/CVPR42600.2020.01164"},{"key":"1841_CR3","doi-asserted-by":"crossref","unstructured":"Chen, X., Ma, H., Wan, J., Li, B., Xia, T.: Multi-view 3D object detection network for autonomous driving. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1907\u20131915. IEEE, Honolulu, HI, USA (2017)","DOI":"10.1109\/CVPR.2017.691"},{"key":"1841_CR4","unstructured":"Dong, R., Tan, Z., Wu, M., Zhang, L., Ma, K.: Finding the task-optimal low-bit sub-distribution in deep neural networks. In: Proceedings of the International Conference on Machine Learning (ICML), pp. 5343\u20135359. PMLR, Baltimore, MD, USA (2022)"},{"issue":"11","key":"1841_CR5","doi-asserted-by":"publisher","first-page":"1231","DOI":"10.1177\/0278364913491297","volume":"32","author":"A Geiger","year":"2013","unstructured":"Geiger, A., Lenz, P., Stiller, C., Urtasun, R.: Vision meets robotics: the KITTI dataset. Int. J. Robot. Res. 32(11), 1231\u20131237 (2013)","journal-title":"Int. J. Robot. Res."},{"issue":"6","key":"1841_CR6","doi-asserted-by":"publisher","first-page":"1789","DOI":"10.1007\/s11263-021-01453-z","volume":"129","author":"J Gou","year":"2021","unstructured":"Gou, J., Yu, B., Maybank, S.J., Tao, D.: Knowledge distillation: a survey. Int. J. Comput. Vis. 129(6), 1789\u20131819 (2021)","journal-title":"Int. J. Comput. Vis."},{"key":"1841_CR7","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1016\/j.neucom.2021.06.046","volume":"459","author":"Y He","year":"2021","unstructured":"He, Y., Xia, G., Luo, Y., Su, L., Zhang, Z., Li, W., Wang, P.: DVFENet: dual-branch voxel feature extraction network for 3D object detection. Neurocomputing 459, 201\u2013211 (2021)","journal-title":"Neurocomputing"},{"key":"1841_CR8","doi-asserted-by":"publisher","unstructured":"Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network. arXiv preprint https:\/\/doi.org\/10.48550\/arXiv.1503.02531 (2015)","DOI":"10.48550\/arXiv.1503.02531"},{"key":"1841_CR9","doi-asserted-by":"crossref","unstructured":"Howard, A., Sandler, M., Chu, G., Chen, L.C., Chen, B., Tan, M., Wang, W., Zhu, Y., Pang, R., Vasudevan, V.: Searching for MobileNetV3. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 1314\u20131324. IEEE\/CVF, Seoul, South Korea (2019)","DOI":"10.1109\/ICCV.2019.00140"},{"issue":"3","key":"1841_CR10","doi-asserted-by":"publisher","first-page":"118","DOI":"10.1007\/s11554-025-01694-y","volume":"22","author":"H Huang","year":"2025","unstructured":"Huang, H., Zhang, S., Fan, H., Wang, T., Jing, Y., Dong, J.: Real-time autonomous underwater and aerial exploration with limited FOV sensors. J. Real-Time Image Proc. 22(3), 118 (2025)","journal-title":"J. Real-Time Image Proc."},{"issue":"14","key":"1841_CR11","doi-asserted-by":"publisher","first-page":"7255","DOI":"10.3390\/app12147255","volume":"12","author":"HK Jung","year":"2022","unstructured":"Jung, H.K., Choi, G.S.: Improved YOLOv5: efficient object detection using drone images under various conditions. Appl. Sci. 12(14), 7255 (2022)","journal-title":"Appl. Sci."},{"key":"1841_CR12","doi-asserted-by":"crossref","unstructured":"Lang, A.H., Vora, S., Caesar, H., Zhou, L., Yang, J., Beijbom, O.: PointPillars: Fast encoders for object detection from point clouds. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 12697\u201312705. IEEE\/CVF, Long Beach, CA, USA (2019)","DOI":"10.1109\/CVPR.2019.01298"},{"key":"1841_CR13","doi-asserted-by":"crossref","unstructured":"Liu, Y., Chen, K., Liu, C., Qin, Z., Luo, Z., Wang, J.: Structured knowledge distillation for semantic segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2604\u20132613. IEEE\/CVF, Long Beach, CA, USA (2019)","DOI":"10.1109\/CVPR.2019.00271"},{"key":"1841_CR14","doi-asserted-by":"crossref","unstructured":"Liu, Z., Mu, H., Zhang, X., Guo, Z., Yang, X., Cheng, K.T., Sun, J.: MetaPruning: Meta learning for automatic neural network channel pruning. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 3296\u20133305. IEEE\/CVF, Seoul, South Korea (2019)","DOI":"10.1109\/ICCV.2019.00339"},{"key":"1841_CR15","doi-asserted-by":"crossref","unstructured":"Park, W., Kim, D., Lu, Y., Cho, M.: Relational knowledge distillation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3967\u20133976. IEEE\/CVF, Long Beach, CA, USA (2019)","DOI":"10.1109\/CVPR.2019.00409"},{"issue":"5","key":"1841_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11554-025-01752-5","volume":"22","author":"J Peng","year":"2025","unstructured":"Peng, J., Zhou, Z., Wang, H.: Lightweight real-time detection transformer with single-head self-attention and feature selection for underwater object detection. J. Real-Time Image Proc. 22(5), 1\u201316 (2025)","journal-title":"J. Real-Time Image Proc."},{"key":"1841_CR17","unstructured":"Qi, C.R., Yi, L., Su, H., Guibas, L.J.: PointNet++: Deep hierarchical feature learning on point sets in a metric space. Adv. Neural Inf. Process. Syst. 30, 5099\u20135108 (2017)"},{"issue":"3","key":"1841_CR18","doi-asserted-by":"publisher","first-page":"33033","DOI":"10.1117\/1.JEI.31.3.033033","volume":"31","author":"J Ren","year":"2022","unstructured":"Ren, J., Wang, Z., Zhang, Y., Liao, L.: YOLOv5-R: lightweight real-time detection based on improved YOLOv5. J. Electron. Imaging 31(3), 33033 (2022)","journal-title":"J. Electron. Imaging"},{"key":"1841_CR19","doi-asserted-by":"publisher","unstructured":"Romero, A., Ballas, N., Kahou, S.E., Chassang, A., Gatta, C., Bengio, Y.: FitNets: Hints for thin deep nets. arXiv preprint https:\/\/doi.org\/10.48550\/arXiv.2405.10931 (2014)","DOI":"10.48550\/arXiv.2405.10931"},{"key":"1841_CR20","doi-asserted-by":"crossref","unstructured":"Shi, S., Wang, X., Li, H.: PointRCNN: 3D object proposal generation and detection from point cloud. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770\u2013779. IEEE\/CVF, Long Beach, CA, USA (2019)","DOI":"10.1109\/CVPR.2019.00086"},{"key":"1841_CR21","doi-asserted-by":"crossref","unstructured":"Tung, F., Mori, G.: Similarity-preserving knowledge distillation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 1365\u20131374. IEEE\/CVF, Seoul, South Korea (2019)","DOI":"10.1109\/ICCV.2019.00145"},{"issue":"5","key":"1841_CR22","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1007\/s11554-025-01740-9","volume":"22","author":"F Wang","year":"2025","unstructured":"Wang, F., Qiao, R., Wang, X., Meng, H.: PRIME-Net: an efficient progressive residual incremental multi-scale estimation network for dynamic ocean wave fields. J. Real-Time Image Proc. 22(5), 171 (2025)","journal-title":"J. Real-Time Image Proc."},{"issue":"2","key":"1841_CR23","doi-asserted-by":"publisher","first-page":"1029","DOI":"10.1007\/s11063-022-10924-4","volume":"55","author":"Y Wu","year":"2023","unstructured":"Wu, Y., Cai, C., Yeo, C.K.: Siamese centerness prediction network for real-time visual object tracking. Neural Process. Lett. 55(2), 1029\u20131044 (2023)","journal-title":"Neural Process. Lett."},{"issue":"10","key":"1841_CR24","doi-asserted-by":"publisher","first-page":"3337","DOI":"10.3390\/s18103337","volume":"18","author":"Y Yan","year":"2018","unstructured":"Yan, Y., Mao, Y., Li, B.: Second: sparsely embedded convolutional detection. Sensors 18(10), 3337 (2018)","journal-title":"Sensors"},{"key":"1841_CR25","first-page":"21300","volume":"35","author":"J Yang","year":"2022","unstructured":"Yang, J., Shi, S., Ding, R., Wang, Z., Qi, X.: Towards efficient 3D object detection with knowledge distillation. Adv. Neural. Inf. Process. Syst. 35, 21300\u201321313 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"1841_CR26","doi-asserted-by":"publisher","unstructured":"Zagoruyko, S., Komodakis, N.: Paying more attention to attention: Improving the performance of convolutional neural networks via attention transfer. arXiv preprint https:\/\/doi.org\/10.48550\/arXiv.1612.03928 (2016)","DOI":"10.48550\/arXiv.1612.03928"},{"key":"1841_CR27","doi-asserted-by":"crossref","unstructured":"Zeng, J., Chen, L., Deng, H., Lu, L., Yan, J., Qiao, Y., Li, H.: Distilling focal knowledge from imperfect expert for 3D object detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 992\u20131001. IEEE\/CVF, Vancouver, BC, Canada (2023)","DOI":"10.1109\/CVPR52729.2023.00102"},{"issue":"1","key":"1841_CR28","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1007\/s00500-021-06407-8","volume":"26","author":"W Zhan","year":"2022","unstructured":"Zhan, W., Sun, C., Wang, M., She, J., Zhang, Y., Zhang, Z., Sun, Y.: An improved Yolov5 real-time detection method for small objects captured by UAV. Soft. Comput. 26(1), 361\u2013373 (2022)","journal-title":"Soft. Comput."},{"key":"1841_CR29","doi-asserted-by":"crossref","unstructured":"Zhang, L., Dong, R., Tai, H.S., Ma, K.: PointDistiller: Structured knowledge distillation towards efficient and compact 3D detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 21791\u201321801. IEEE\/CVF, Vancouver, BC, Canada (2023)","DOI":"10.1109\/CVPR52729.2023.02087"},{"key":"1841_CR30","unstructured":"Zhang, L., Ma, K.: Improve object detection with feature-based knowledge distillation: towards accurate and efficient detectors. In: International Conference on Learning Representations (ICLR). OpenReview.net, Vienna, Austria (2021)"},{"issue":"7","key":"1841_CR31","doi-asserted-by":"publisher","first-page":"1681","DOI":"10.1109\/JAS.2024.124275","volume":"11","author":"Q Zhang","year":"2024","unstructured":"Zhang, Q., Wang, L., Meng, H., Zhang, W., Huang, G.: A LiDAR point clouds dataset of ships in a maritime environment. IEEE\/CAA J. Autom. Sin. 11(7), 1681\u20131694 (2024)","journal-title":"IEEE\/CAA J. Autom. Sin."},{"key":"1841_CR32","doi-asserted-by":"crossref","unstructured":"Zhao, B., Cui, Q., Song, R., Qiu, Y., Liang, J.: Decoupled knowledge distillation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 11953\u201311962. IEEE\/CVF, New Orleans, LA, USA (2022)","DOI":"10.1109\/CVPR52688.2022.01165"}],"container-title":["Journal of Real-Time Image Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11554-025-01841-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11554-025-01841-5","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11554-025-01841-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T07:32:43Z","timestamp":1778484763000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11554-025-01841-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,6]]},"references-count":32,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2026,4]]}},"alternative-id":["1841"],"URL":"https:\/\/doi.org\/10.1007\/s11554-025-01841-5","relation":{},"ISSN":["1861-8200","1861-8219"],"issn-type":[{"value":"1861-8200","type":"print"},{"value":"1861-8219","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,6]]},"assertion":[{"value":"31 August 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 December 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 February 2026","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 competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"60"}}