{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T20:07:31Z","timestamp":1766088451179,"version":"3.44.0"},"reference-count":47,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T00:00:00Z","timestamp":1742860800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T00:00:00Z","timestamp":1742860800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"the  Beijing Smarter Eye Technology Co., Ltd","award":["2200010047","2200010047","2200010047","2200010047"],"award-info":[{"award-number":["2200010047","2200010047","2200010047","2200010047"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Vis Comput"],"published-print":{"date-parts":[[2025,8]]},"DOI":"10.1007\/s00371-025-03847-3","type":"journal-article","created":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T07:06:53Z","timestamp":1743059213000},"page":"7939-7950","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["LF-RTMDet: an instance segmentation algorithm for real-time detection of water-filled barriers"],"prefix":"10.1007","volume":"41","author":[{"given":"JiaHao","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongqiang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Congling","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiawei","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,3,25]]},"reference":[{"key":"3847_CR1","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1016\/j.ijimpeng.2014.04.008","volume":"72","author":"M Thiyahuddin","year":"2014","unstructured":"Thiyahuddin, M., Gu, Y., Thambiratnam, D., Thilakarathna, H.: Impact and energy absorption of portable water-filled road safety barrier system fitted with foam. Int. J. Impact Eng. 72, 26\u201339 (2014)","journal-title":"Int. J. Impact Eng."},{"key":"3847_CR2","doi-asserted-by":"crossref","unstructured":"McAllister, R.T., Gal, Y., Kendall, A., Van Der\u00a0Wilk, M., Shah, A., Cipolla, R., Weller, A.: Concrete problems for autonomous vehicle safety: advantages of bayesian deep learning. In: International Joint Conferences on Artificial Intelligence, Inc (2017)","DOI":"10.24963\/ijcai.2017\/661"},{"issue":"6","key":"3847_CR3","doi-asserted-by":"publisher","first-page":"499","DOI":"10.1007\/s42979-022-01407-3","volume":"3","author":"R Sharma","year":"2022","unstructured":"Sharma, R., Saqib, M., Lin, C.-T., Blumenstein, M.: A survey on object instance segmentation. SN Comput. Sci. 3(6), 499 (2022)","journal-title":"SN Comput. Sci."},{"key":"3847_CR4","first-page":"1","volume":"61","author":"Y Xue","year":"2023","unstructured":"Xue, Y., Jin, G., Shen, T., Tan, L., Wang, N., Gao, J., Wang, L.: Smalltrack: Wavelet pooling and graph enhanced classification for UAV small object tracking. IEEE Trans. Geosci. Remote Sens. 61, 1\u201315 (2023)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"9","key":"3847_CR5","doi-asserted-by":"publisher","first-page":"299","DOI":"10.1016\/j.cja.2023.03.048","volume":"36","author":"Y Xue","year":"2023","unstructured":"Xue, Y., Jin, G., Shen, T., Tan, L., Wang, L.: Template-guided frequency attention and adaptive cross-entropy loss for UAV visual tracking. Chin. J. Aeronaut. 36(9), 299\u2013312 (2023)","journal-title":"Chin. J. Aeronaut."},{"issue":"12","key":"3847_CR6","doi-asserted-by":"publisher","first-page":"3300","DOI":"10.1049\/ipr2.12565","volume":"16","author":"Y Xue","year":"2022","unstructured":"Xue, Y., Jin, G., Shen, T., Tan, L., Yang, J., Hou, X.: Mobiletrack: Siamese efficient mobile network for high-speed UAV tracking. IET Image Proc. 16(12), 3300\u20133313 (2022)","journal-title":"IET Image Proc."},{"key":"3847_CR7","doi-asserted-by":"crossref","unstructured":"Xue, Y., Jin, G., Shen, T., Tan, L., Wang, N., Gao, J., Wang, L.: Consistent representation mining for multi-drone single object tracking. IEEE Trans. Circuits Syst. Video Technol. (2024)","DOI":"10.1109\/TCSVT.2024.3411301"},{"key":"3847_CR8","doi-asserted-by":"crossref","unstructured":"Xue, Y., Shen, T., Jin, G., Tan, L., Wang, N., Wang, L., Gao, J.: Handling occlusion in UAV visual tracking with query-guided redetection. IEEE Trans. Instrum. Measurement (2024)","DOI":"10.1109\/TIM.2024.3440378"},{"key":"3847_CR9","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)"},{"key":"3847_CR10","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"3847_CR11","doi-asserted-by":"crossref","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, pp. 10012\u201310022 (2021)","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"3847_CR12","doi-asserted-by":"crossref","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, pp. 6154\u20136162 (2018)","DOI":"10.1109\/CVPR.2018.00644"},{"key":"3847_CR13","doi-asserted-by":"crossref","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)","DOI":"10.1109\/CVPR.2016.91"},{"key":"3847_CR14","unstructured":"Zhang, H., Li, F., Liu, S., Zhang, L., Su, H., Zhu, J., Ni, L.M., Shum, H.-Y.: Dino: Detr with improved denoising anchor boxes for end-to-end object detection. arXiv preprint arXiv:2203.03605 (2022)"},{"key":"3847_CR15","first-page":"1140","volume":"35","author":"M-H Guo","year":"2022","unstructured":"Guo, M.-H., Lu, C.-Z., Hou, Q., Liu, Z., Cheng, M.-M., Hu, S.-M.: SegNeXt: Rethinking convolutional attention design for semantic segmentation. Adv. Neural. Inf. Process. Syst. 35, 1140\u20131156 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"3847_CR16","first-page":"12077","volume":"34","author":"E Xie","year":"2021","unstructured":"Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: SegFormer: Simple and efficient design for semantic segmentation with transformers. Adv. Neural. Inf. Process. Syst. 34, 12077\u201312090 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"issue":"3","key":"3847_CR17","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1007\/s13735-020-00195-x","volume":"9","author":"AM Hafiz","year":"2020","unstructured":"Hafiz, A.M., Bhat, G.M.: A survey on instance segmentation: state of the art. Int. J. Multimed. Inf. Retr. 9(3), 171\u2013189 (2020)","journal-title":"Int. J. Multimed. Inf. Retr."},{"key":"3847_CR18","doi-asserted-by":"crossref","unstructured":"Chen, L.-C., Hermans, A., Papandreou, G., Schroff, F., Wang, P., Adam, H.: MaskLab: Instance segmentation by refining object detection with semantic and direction features. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4013\u20134022 (2018)","DOI":"10.1109\/CVPR.2018.00422"},{"key":"3847_CR19","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., Girshick, R.: Mask R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961\u20132969 (2017)","DOI":"10.1109\/ICCV.2017.322"},{"key":"3847_CR20","doi-asserted-by":"crossref","unstructured":"Wang, X., Kong, T., Shen, C., Jiang, Y., Li, L.: Solo: Segmenting objects by locations. In: Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part XVIII 16, pp. 649\u2013665. Springer (2020)","DOI":"10.1007\/978-3-030-58523-5_38"},{"key":"3847_CR21","unstructured":"Lyu, C., Zhang, W., Huang, H., Zhou, Y., Wang, Y., Liu, Y., Zhang, S., Chen, K.: Rtmdet: An empirical study of designing real-time object detectors. arXiv preprint arXiv:2212.07784 (2022)"},{"key":"3847_CR22","doi-asserted-by":"crossref","unstructured":"Han, K., Wang, Y., Tian, Q., Guo, J., Xu, C., Xu, C.: Ghostnet: More features from cheap operations. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1580\u20131589 (2020)","DOI":"10.1109\/CVPR42600.2020.00165"},{"key":"3847_CR23","doi-asserted-by":"crossref","unstructured":"Wang, J., Chen, K., Xu, R., Liu, Z., Loy, C.C., Lin, D.: Carafe: Content-aware reassembly of features. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 3007\u20133016 (2019)","DOI":"10.1109\/ICCV.2019.00310"},{"key":"3847_CR24","doi-asserted-by":"crossref","unstructured":"Liu, H., Liu, F., Fan, X., Huang, D.: Polarized self-attention: Towards high-quality pixel-wise regression. arXiv preprint arXiv:2107.00782 (2021)","DOI":"10.1016\/j.neucom.2022.07.054"},{"key":"3847_CR25","doi-asserted-by":"publisher","first-page":"104752","DOI":"10.1016\/j.dsp.2024.104752","volume":"155","author":"Y Liu","year":"2024","unstructured":"Liu, Y., Wang, Y., Li, Q.: Lane detection based on real-time semantic segmentation for end-to-end autonomous driving under low-light conditions. Digital Signal Process. 155, 104752 (2024)","journal-title":"Digital Signal Process."},{"key":"3847_CR26","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1016\/j.cag.2022.09.001","volume":"108","author":"H Jia","year":"2022","unstructured":"Jia, H., Xiao, Z., Ji, P.: Real-time fatigue driving detection system based on multi-module fusion. Comput. Graphics 108, 22\u201333 (2022)","journal-title":"Comput. Graphics"},{"issue":"2","key":"3847_CR27","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1007\/s11554-023-01403-7","volume":"21","author":"Y Luo","year":"2024","unstructured":"Luo, Y., Ci, Y., Jiang, S., Wei, X.: A novel lightweight real-time traffic sign detection method based on an embedded device and YOLOv8. J. Real-Time Image Proc. 21(2), 24 (2024)","journal-title":"J. Real-Time Image Proc."},{"key":"3847_CR28","doi-asserted-by":"publisher","first-page":"104206","DOI":"10.1016\/j.jvcir.2024.104206","volume":"102","author":"X Lu","year":"2024","unstructured":"Lu, X., Xue, Y., Wang, Z., Xu, H., Wen, X.: X-CDNet: a real-time crosswalk detector based on YOLOX. J. Vis. Commun. Image Represent. 102, 104206 (2024)","journal-title":"J. Vis. Commun. Image Represent."},{"key":"3847_CR29","doi-asserted-by":"crossref","unstructured":"Jin, H., Lan, Z., He, X.: On highway guardrail detection algorithm based on mask RCNN in complex environments. In: 2021 7th International Conference on Systems and Informatics (ICSAI), pp. 1\u20136. IEEE (2021)","DOI":"10.1109\/ICSAI53574.2021.9664044"},{"key":"3847_CR30","doi-asserted-by":"crossref","unstructured":"Jacob, B., Kligys, S., Chen, B., Zhu, M., Tang, M., Howard, A., Adam, H., Kalenichenko, D.: Quantization and training of neural networks for efficient integer-arithmetic-only inference. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2704\u20132713 (2018)","DOI":"10.1109\/CVPR.2018.00286"},{"key":"3847_CR31","doi-asserted-by":"crossref","unstructured":"Yang, J., Shen, X., Xing, J., Tian, X., Li, H., Deng, B., Huang, J., Hua, X.-S.: Quantization networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7308\u20137316 (2019)","DOI":"10.1109\/CVPR.2019.00748"},{"key":"3847_CR32","doi-asserted-by":"crossref","unstructured":"Kwasniewska, A., Szankin, M., Ozga, M., Wolfe, J., Das, A., Zajac, A., Ruminski, J., Rad, P.: Deep learning optimization for edge devices: Analysis of training quantization parameters. In: IECON 2019-45th Annual Conference of the IEEE Industrial Electronics Society, vol. 1, pp. 96\u2013101. IEEE (2019)","DOI":"10.1109\/IECON.2019.8927153"},{"key":"3847_CR33","doi-asserted-by":"publisher","first-page":"281","DOI":"10.1016\/j.procs.2023.11.032","volume":"228","author":"H Huang","year":"2023","unstructured":"Huang, H., Pan, B., Wang, L., Jiang, C.: Quantization method integrated with progressive quantization and distillation learning. Proced. Comput. Sci. 228, 281\u2013290 (2023)","journal-title":"Proced. Comput. Sci."},{"key":"3847_CR34","doi-asserted-by":"crossref","unstructured":"Jiang, H., Li, Q., Li, Y.: Post training quantization after neural network. In: 2022 14th International Conference on Computer Research and Development (ICCRD), pp. 1\u20136. IEEE (2022)","DOI":"10.1109\/ICCRD54409.2022.9730411"},{"key":"3847_CR35","unstructured":"Krishnamoorthi, R.: Quantizing deep convolutional networks for efficient inference: A whitepaper. arXiv preprint arXiv:1806.08342 (2018)"},{"key":"3847_CR36","doi-asserted-by":"publisher","first-page":"105254","DOI":"10.1016\/j.autcon.2023.105254","volume":"159","author":"X Yang","year":"2024","unstructured":"Yang, X., Rey Castillo, E., Zou, Y., Wotherspoon, L.: UAV-deployed deep learning network for real-time multi-class damage detection using model quantization techniques. Autom. Constr. 159, 105254 (2024)","journal-title":"Autom. Constr."},{"key":"3847_CR37","doi-asserted-by":"crossref","unstructured":"Horowitz, M.: 1.1 computing\u2019s energy problem (and what we can do about it). In: 2014 IEEE International Solid-state Circuits Conference Digest of Technical Papers (ISSCC), pp. 10\u201314. IEEE (2014)","DOI":"10.1109\/ISSCC.2014.6757323"},{"key":"3847_CR38","unstructured":"Li, X., Hu, X., Yang, J.: Spatial group-wise enhance: Improving semantic feature learning in convolutional networks. arXiv preprint arXiv:1905.09646 (2019)"},{"key":"3847_CR39","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, pp. 11863\u201311874. PMLR (2021)"},{"key":"3847_CR40","first-page":"1140","volume":"35","author":"M-H Guo","year":"2022","unstructured":"Guo, M.-H., Lu, C.-Z., Hou, Q., Liu, Z., Cheng, M.-M., Hu, S.-M.: SegNeXt: rethinking convolutional attention design for semantic segmentation. Adv. Neural. Inf. Process. Syst. 35, 1140\u20131156 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"3847_CR41","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., Girshick, R.: Mask R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961\u20132969 (2017)","DOI":"10.1109\/ICCV.2017.322"},{"issue":"5","key":"3847_CR42","doi-asserted-by":"publisher","first-page":"1483","DOI":"10.1109\/TPAMI.2019.2956516","volume":"43","author":"Z Cai","year":"2019","unstructured":"Cai, Z., Vasconcelos, N.: Cascade R-CNN: high quality object detection and instance segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 43(5), 1483\u20131498 (2019)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"3847_CR43","doi-asserted-by":"crossref","unstructured":"Wang, X., Kong, T., Shen, C., Jiang, Y., Li, L.: Solo: Segmenting objects by locations. In: Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part XVIII 16, pp. 649\u2013665. Springer (2020)","DOI":"10.1007\/978-3-030-58523-5_38"},{"key":"3847_CR44","first-page":"17721","volume":"33","author":"X Wang","year":"2020","unstructured":"Wang, X., Zhang, R., Kong, T., Li, L., Shen, C.: Solov2: dynamic and fast instance segmentation. Adv. Neural. Inf. Process. Syst. 33, 17721\u201317732 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"3847_CR45","doi-asserted-by":"crossref","unstructured":"Kirillov, A., Wu, Y., He, K., Girshick, R.: Pointrend: Image segmentation as rendering. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9799\u20139808 (2020)","DOI":"10.1109\/CVPR42600.2020.00982"},{"key":"3847_CR46","doi-asserted-by":"crossref","unstructured":"Woo, S., Debnath, S., Hu, R., Chen, X., Liu, Z., Kweon, I.S., Xie, S.: Convnext v2: Co-designing and scaling convnets with masked autoencoders. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 16133\u201316142 (2023)","DOI":"10.1109\/CVPR52729.2023.01548"},{"key":"3847_CR47","doi-asserted-by":"crossref","unstructured":"Cheng, T., Wang, X., Chen, S., Zhang, W., Zhang, Q., Huang, C., Zhang, Z., Liu, W.: Sparse instance activation for real-time instance segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4433\u20134442 (2022)","DOI":"10.1109\/CVPR52688.2022.00439"}],"container-title":["The Visual Computer"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-025-03847-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00371-025-03847-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-025-03847-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,6]],"date-time":"2025-09-06T08:08:25Z","timestamp":1757146105000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00371-025-03847-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,25]]},"references-count":47,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2025,8]]}},"alternative-id":["3847"],"URL":"https:\/\/doi.org\/10.1007\/s00371-025-03847-3","relation":{},"ISSN":["0178-2789","1432-2315"],"issn-type":[{"type":"print","value":"0178-2789"},{"type":"electronic","value":"1432-2315"}],"subject":[],"published":{"date-parts":[[2025,3,25]]},"assertion":[{"value":"10 February 2025","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 March 2025","order":2,"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 that there are no conflict of interest regarding the publication of this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}