{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T12:20:34Z","timestamp":1770985234940,"version":"3.50.1"},"reference-count":62,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,1,18]],"date-time":"2026-01-18T00:00:00Z","timestamp":1768694400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,18]],"date-time":"2026-01-18T00:00:00Z","timestamp":1768694400000},"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":"publisher","award":["52575100"],"award-info":[{"award-number":["52575100"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Real-Time Image Proc"],"published-print":{"date-parts":[[2026,2]]},"DOI":"10.1007\/s11554-025-01849-x","type":"journal-article","created":{"date-parts":[[2026,1,18]],"date-time":"2026-01-18T11:10:10Z","timestamp":1768734610000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["SBP-YOLO: a lightweight real-time model for detecting speed bumps and potholes toward intelligent vehicle suspension systems"],"prefix":"10.1007","volume":"23","author":[{"given":"Chuanqi","family":"Liang","sequence":"first","affiliation":[]},{"given":"Jie","family":"Yuan","sequence":"additional","affiliation":[]},{"given":"Linlin","family":"Gou","sequence":"additional","affiliation":[]},{"given":"Lei","family":"Luo","sequence":"additional","affiliation":[]},{"given":"Jie","family":"Fu","sequence":"additional","affiliation":[]},{"given":"Miao","family":"Yu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,1,18]]},"reference":[{"key":"1849_CR1","doi-asserted-by":"publisher","unstructured":"Li, W., Du, X., Xia, D., Fu, J., Yu, M.: Robust $$h_\\infty$$ control for magnetorheological suspension system of all-terrain vehicles with parameter uncertainties and time delay. Veh. Syst. Dyn. 1\u201326 (2025). https:\/\/doi.org\/10.1080\/00423114.2025.2501993","DOI":"10.1080\/00423114.2025.2501993"},{"key":"1849_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2025.112803","volume":"234","author":"M Yu","year":"2025","unstructured":"Yu, M., Xia, D., Li, W., Han, G., Du, X., Fu, J.: Research on finite frequency robust $$h_\\infty$$ control of all-terrain vehicle magnetorheological suspension system. Mech. Syst. Signal Process. 234, 112803 (2025)","journal-title":"Mech. Syst. Signal Process."},{"key":"1849_CR3","doi-asserted-by":"publisher","first-page":"160","DOI":"10.1016\/j.eng.2023.06.014","volume":"33","author":"M Yu","year":"2024","unstructured":"Yu, M., Evangelou, S.A., Dini, D.: Advances in active suspension systems for road vehicles. Engineering 33, 160\u2013177 (2024)","journal-title":"Engineering"},{"issue":"11","key":"1849_CR4","doi-asserted-by":"publisher","first-page":"3908","DOI":"10.1007\/s40435-024-01489-2","volume":"12","author":"AA Ferhath","year":"2024","unstructured":"Ferhath, A.A., Kasi, K.: The evolution of damper technology for enhanced ride comfort and vehicle handling in vehicle suspension system. Int. J. Dyn. Control 12(11), 3908\u20133946 (2024)","journal-title":"Int. J. Dyn. Control"},{"key":"1849_CR5","unstructured":"Wambold, J.C., Zimmer, R.A., Ivey, D., Sicking, D.L.: Roughness, holes, and bumps. In: Roadway Surface Discontinuities on Safety, vol.\u00a011 (2009)"},{"issue":"7","key":"1849_CR6","doi-asserted-by":"publisher","first-page":"10 669","DOI":"10.1109\/JSEN.2024.3362737","volume":"24","author":"Y Yin","year":"2024","unstructured":"Yin, Y., Fu, W., Ma, X., Yu, J., Li, X., Dong, Z.: Road surface pits and speed bumps recognition based on acceleration sensor. IEEE Sens. J. 24(7), 10 669-10 679 (2024)","journal-title":"IEEE Sens. J."},{"issue":"3","key":"1849_CR7","doi-asserted-by":"publisher","first-page":"127","DOI":"10.3390\/a18030127","volume":"18","author":"A Aguilar-Gonz\u00e1lez","year":"2025","unstructured":"Aguilar-Gonz\u00e1lez, A., Medina Santiago, A.: Road event detection and classification algorithm using vibration and acceleration data. Algorithms 18(3), 127 (2025)","journal-title":"Algorithms"},{"key":"1849_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.conengprac.2023.105553","volume":"137","author":"A Achnib","year":"2023","unstructured":"Achnib, A., Sename, O.: Discrete-time multi-model preview control: application to a real semi-active automotive suspension system. Control. Eng. Pract. 137, 105553 (2023)","journal-title":"Control. Eng. Pract."},{"issue":"3","key":"1849_CR9","doi-asserted-by":"publisher","DOI":"10.1088\/1361-665X\/ad21b3","volume":"33","author":"W Li","year":"2024","unstructured":"Li, W., Liang, H., Xia, D., Fu, J., Yu, M.: Explicit model predictive control of magnetorheological suspension for all-terrain vehicles with road preview. Smart Mater. Struct. 33(3), 035037 (2024)","journal-title":"Smart Mater. Struct."},{"issue":"4","key":"1849_CR10","doi-asserted-by":"publisher","first-page":"1115","DOI":"10.1007\/s12239-025-00212-0","volume":"26","author":"JY Jung","year":"2025","unstructured":"Jung, J.Y., Lee, C.: Preview model predictive control of semi-active suspension for speed bump. Int. J. Automot. Technol. 26(4), 1115\u20131126 (2025)","journal-title":"Int. J. Automot. Technol."},{"issue":"1","key":"1849_CR11","doi-asserted-by":"publisher","first-page":"9221211","DOI":"10.1155\/2022\/9221211","volume":"2022","author":"MH Asad","year":"2022","unstructured":"Asad, M.H., Khaliq, S., Yousaf, M.H., Ullah, M.O., Ahmad, A.: Pothole detection using deep learning: a real-time and AI-on-the-edge perspective. Adv. Civ. Eng. 2022(1), 9221211 (2022)","journal-title":"Adv. Civ. Eng."},{"issue":"15","key":"1849_CR12","doi-asserted-by":"publisher","first-page":"24 802","DOI":"10.1109\/JSEN.2024.3399008","volume":"24","author":"N Bhavana","year":"2024","unstructured":"Bhavana, N., Kodabagi, M.M., Kumar, B.M., Ajay, P., Muthukumaran, N., Ahilan, A.: POT-YOLO: real-time road potholes detection using edge segmentation-based Yolo v8 network. IEEE Sens. J. 24(15), 24 802-24 809 (2024)","journal-title":"IEEE Sens. J."},{"key":"1849_CR13","doi-asserted-by":"crossref","unstructured":"Liu, S., Zha, J., Sun, J., Li, Z., Wang, G.: EdgeYOLO: an edge-real-time object detector. In: 42nd Chinese Control Conference (CCC), vol. 2023, pp. 7507\u20137512. IEEE (2023)","DOI":"10.23919\/CCC58697.2023.10239786"},{"key":"1849_CR14","doi-asserted-by":"publisher","DOI":"10.1177\/03019233241266717","author":"J Zhao","year":"2024","unstructured":"Zhao, J., Liu, S., Tao, H., Liu, W.: Slim-YOLOv8: a fast and accurate algorithm for surface defect detection of steel strips. Ironmak. Steelmak. (2024). https:\/\/doi.org\/10.1177\/03019233241266717","journal-title":"Ironmak. Steelmak."},{"issue":"22","key":"1849_CR15","doi-asserted-by":"publisher","first-page":"8878","DOI":"10.3390\/s22228878","volume":"22","author":"B Bu\u010dko","year":"2022","unstructured":"Bu\u010dko, B., Lieskovsk\u00e1, E., Z\u00e1bovsk\u00e1, K., Z\u00e1bovsk\u1ef3, M.: Computer vision based pothole detection under challenging conditions. Sensors 22(22), 8878 (2022)","journal-title":"Sensors"},{"issue":"5","key":"1849_CR16","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1007\/s11554-024-01545-2","volume":"21","author":"S Youwai","year":"2024","unstructured":"Youwai, S., Chaiyaphat, A., Chaipetch, P.: YOLO9tr: a lightweight model for pavement damage detection utilizing a generalized efficient layer aggregation network and attention mechanism. J. Real-Time Image Proc. 21(5), 163 (2024)","journal-title":"J. Real-Time Image Proc."},{"key":"1849_CR17","unstructured":"Lyu, R.: NanoDet-Plus: super fast and high accuracy lightweight anchor-free object detection model. https:\/\/github.com\/RangiLyu\/nanodet (2021)"},{"key":"1849_CR18","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580\u2013587 (2014)","DOI":"10.1109\/CVPR.2014.81"},{"key":"1849_CR19","doi-asserted-by":"crossref","unstructured":"Girshick, R.: Fast R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1440\u20131448 (2015)","DOI":"10.1109\/ICCV.2015.169"},{"issue":"6","key":"1849_CR20","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)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1849_CR21","unstructured":"Redmon, J., Farhadi, A.: YOLOv3: an incremental improvement. arXiv preprint arXiv:1804.02767 (2018)"},{"key":"1849_CR22","unstructured":"Jocher, G., Stoken, A., Borovec, J., Changyu, L., Hogan, A., Diaconu, L., Poznanski, J., Yu, L., Rai, P., Ferriday, R., et\u00a0al.: Ultralytics\/YOLOv5: v3. 0. Zenodo (2020)"},{"key":"1849_CR23","unstructured":"Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., Ke, Z., Li, Q., Cheng, M., Nie, W., et\u00a0al.: YOLOv6: a single-stage object detection framework for industrial applications. arXiv preprint arXiv:2209.02976 (2022)"},{"key":"1849_CR24","doi-asserted-by":"crossref","unstructured":"Sohan, M., Sai\u00a0Ram, T., Rami\u00a0Reddy, C.V.: A review on yolov8 and its advancements. In: International Conference on Data Intelligence and Cognitive Informatics, pp. 529\u2013545. Springer (2024)","DOI":"10.1007\/978-981-99-7962-2_39"},{"key":"1849_CR25","doi-asserted-by":"crossref","unstructured":"Wang, C.-Y., Yeh, I.-H., Mark\u00a0Liao, H.-Y.: YOLOv9: learning what you want to learn using programmable gradient information. In: European Conference on Computer Vision, pp. 1\u201321. Springer (2024)","DOI":"10.1007\/978-3-031-72751-1_1"},{"key":"1849_CR26","first-page":"107984","volume":"37","author":"A Wang","year":"2024","unstructured":"Wang, A., Chen, H., Liu, L., Chen, K., Lin, Z., Han, J., et al.: YOLOv10: real-time end-to-end object detection. Adv. Neural. Inf. Process. Syst. 37, 107984\u2013108011 (2024)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"1849_CR27","unstructured":"Khanam, R., Hussain, M.: YOLOv11: an overview of the key architectural enhancements. arXiv preprint arXiv:2410.17725 (2024)"},{"issue":"4","key":"1849_CR28","doi-asserted-by":"publisher","first-page":"1126","DOI":"10.4236\/ojapps.2025.154078","volume":"15","author":"M Chileshe","year":"2025","unstructured":"Chileshe, M., Nyirenda, M., Kaoma, J.: Early detection of sexually transmitted infections using YOLO 12: a deep learning approach. Open J. Appl. Sci. 15(4), 1126\u20131144 (2025)","journal-title":"Open J. Appl. Sci."},{"key":"1849_CR29","unstructured":"Lei, M., Li, S., Wu, Y., Hu, H., Zhou, Y., Zheng, X., Ding, G., Du, S., Wu, Z., Gao, Y.: YOLOv13: real-time object detection with hypergraph-enhanced adaptive visual perception. arXiv preprint arXiv:2506.17733 (2025)"},{"key":"1849_CR30","doi-asserted-by":"crossref","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, pp. 213\u2013229. Springer (2020)","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"1849_CR31","doi-asserted-by":"crossref","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. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 16965\u201316974 (2024)","DOI":"10.1109\/CVPR52733.2024.01605"},{"key":"1849_CR32","doi-asserted-by":"publisher","first-page":"2388","DOI":"10.1109\/TPAMI.2024.3524377","volume":"47","author":"Y Feng","year":"2024","unstructured":"Feng, Y., Huang, J., Du, S., Ying, S., Yong, J.-H., Li, Y., Ding, G., Ji, R., Gao, Y.: Hyper-YOLO: when visual object detection meets hypergraph computation. IEEE Trans. Pattern Anal. Mach. Intell. 47, 2388\u20132401 (2024)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1849_CR33","unstructured":"Wang, Z., Li, C., Xu, H., Zhu, X.: Mamba YOLO: SSMs-based YOLO for object detection. arXiv e-prints, pp. arXiv-2406 (2024)"},{"issue":"23","key":"1849_CR34","doi-asserted-by":"publisher","first-page":"11229","DOI":"10.3390\/app112311229","volume":"11","author":"S-S Park","year":"2021","unstructured":"Park, S.-S., Tran, V.-T., Lee, D.-E.: Application of various yolo models for computer vision-based real-time pothole detection. Appl. Sci. 11(23), 11229 (2021)","journal-title":"Appl. Sci."},{"issue":"7","key":"1849_CR35","doi-asserted-by":"publisher","first-page":"2130","DOI":"10.3390\/s24072130","volume":"24","author":"R Wang","year":"2024","unstructured":"Wang, R., Luo, X., Ye, Q., Jiang, Y., Liu, W.: Research on visual perception of speed bumps for intelligent connected vehicles based on lightweight FPNet. Sensors 24(7), 2130 (2024)","journal-title":"Sensors"},{"key":"1849_CR36","doi-asserted-by":"publisher","first-page":"1323792","DOI":"10.3389\/fbuil.2023.1323792","volume":"9","author":"M Khan","year":"2024","unstructured":"Khan, M., Raza, M.A., Abbas, G., Othmen, S., Yousef, A., Jumani, T.A.: Pothole detection for autonomous vehicles using deep learning: a robust and efficient solution. Front. Built Environ. 9, 1323792 (2024)","journal-title":"Front. Built Environ."},{"issue":"3","key":"1849_CR37","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/LSENS.2024.3360095","volume":"8","author":"DK Dewangan","year":"2024","unstructured":"Dewangan, D.K., Sahu, S.P., Arya, K.V.: Vision-sensor enabled multilayer CNN scheme and impact analysis of learning rate parameter for speed bump detection in autonomous vehicle system. IEEE Sens. Lett. 8(3), 1\u20134 (2024)","journal-title":"IEEE Sens. Lett."},{"issue":"14","key":"1849_CR38","doi-asserted-by":"publisher","first-page":"8349","DOI":"10.3390\/app13148349","volume":"13","author":"J-E Peralta-L\u00f3pez","year":"2023","unstructured":"Peralta-L\u00f3pez, J.-E., Morales-Viscaya, J.-A., L\u00e1zaro-Mata, D., Villase\u00f1or-Aguilar, M.-J., Prado-Olivarez, J., P\u00e9rez-Pinal, F.-J., Padilla-Medina, J.-A., Mart\u00ednez-Nolasco, J.-J., Barranco-Guti\u00e9rrez, A.-I.: Speed bump and pothole detection using deep neural network with images captured through zed camera. Appl. Sci. 13(14), 8349 (2023)","journal-title":"Appl. Sci."},{"key":"1849_CR39","doi-asserted-by":"crossref","unstructured":"Chandak,P., Chaurasia, S., Patil, M.: Identification of potholes and speed bumps using SSD and YOLO. In: International Conference on Intelligent Computing and Networking, pp. 1\u201320. Springer (2024)","DOI":"10.1007\/978-981-97-8631-2_1"},{"issue":"17","key":"1849_CR40","doi-asserted-by":"publisher","first-page":"5652","DOI":"10.3390\/s24175652","volume":"24","author":"Y Safyari","year":"2024","unstructured":"Safyari, Y., Mahdianpari, M., Shiri, H.: A review of vision-based pothole detection methods using computer vision and machine learning. Sensors 24(17), 5652 (2024)","journal-title":"Sensors"},{"issue":"18","key":"1849_CR41","doi-asserted-by":"publisher","first-page":"5884","DOI":"10.3390\/s25185884","volume":"25","author":"Y Shen","year":"2025","unstructured":"Shen, Y., Jing, K., Sun, K., Liu, C., Yang, Y., Liu, Y.: Review of uneven road surface information perception methods for suspension preview control. Sensors 25(18), 5884 (2025)","journal-title":"Sensors"},{"issue":"12","key":"1849_CR42","doi-asserted-by":"publisher","first-page":"24 330","DOI":"10.1109\/TITS.2022.3203715","volume":"23","author":"J Shen","year":"2022","unstructured":"Shen, J., Zhou, W., Liu, N., Sun, H., Li, D., Zhang, Y.: An anchor-free lightweight deep convolutional network for vehicle detection in aerial images. IEEE Trans. Intell. Transp. Syst. 23(12), 24 330-24 342 (2022)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"1849_CR43","first-page":"1","volume":"73","author":"J Shen","year":"2024","unstructured":"Shen, J., Liu, N., Sun, H., Li, D., Zhang, Y.: An instrument indication acquisition algorithm based on lightweight deep convolutional neural network and hybrid attention fine-grained features. IEEE Trans. Instrum. Meas. 73, 1\u201316 (2024)","journal-title":"IEEE Trans. Instrum. Meas."},{"issue":"1","key":"1849_CR44","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1038\/s40494-025-01565-6","volume":"13","author":"J Shen","year":"2025","unstructured":"Shen, J., Liu, N., Sun, H., Li, D., Zhang, Y., Han, L.: An algorithm based on lightweight semantic features for ancient mural element object detection. NPJ Herit. Sci. 13(1), 70 (2025)","journal-title":"NPJ Herit. Sci."},{"key":"1849_CR45","first-page":"1","volume":"71","author":"J Shen","year":"2021","unstructured":"Shen, J., Liu, N., Xu, C., Sun, H., Xiao, Y., Li, D., Zhang, Y.: Finger vein recognition algorithm based on lightweight deep convolutional neural network. IEEE Trans. Instrum. Meas. 71, 1\u201313 (2021)","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"1849_CR46","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"},{"issue":"3","key":"1849_CR47","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1007\/s11554-024-01436-6","volume":"21","author":"H Li","year":"2024","unstructured":"Li, H., Li, J., Wei, H., Liu, Z., Zhan, Z., Ren, Q.: Slim-neck by GSConv: a lightweight-design for real-time detector architectures. J. Real-Time Image Proc. 21(3), 62 (2024)","journal-title":"J. Real-Time Image Proc."},{"key":"1849_CR48","unstructured":"Wang, J., Xu, C., Yang, W., Yu, L.: A normalized Gaussian Wasserstein distance for tiny object detection. arXiv preprint arXiv:2110.13389 (2021)"},{"key":"1849_CR49","unstructured":"Nienaber, S., Booysen, M., Kroon, R.: Detecting potholes using simple image processing techniques and real-world footage. In: Southern African Transport Conference (2015)"},{"key":"1849_CR50","doi-asserted-by":"publisher","first-page":"988","DOI":"10.1016\/j.procs.2018.10.335","volume":"143","author":"V Varma","year":"2018","unstructured":"Varma, V., Adarsh, S., Ramachandran, K., Nair, B.B.: Real time detection of speed hump\/bump and distance estimation with deep learning using GPU and zed stereo camera. Procedia Comput. Sci. 143, 988\u2013997 (2018)","journal-title":"Procedia Comput. Sci."},{"issue":"2","key":"1849_CR51","doi-asserted-by":"publisher","first-page":"125","DOI":"10.3390\/info11020125","volume":"11","author":"A Buslaev","year":"2020","unstructured":"Buslaev, A., Iglovikov, V.I., Khvedchenya, E., Parinov, A., Druzhinin, M., Kalinin, A.A.: Albumentations: fast and flexible image augmentations. Information 11(2), 125 (2020)","journal-title":"Information"},{"key":"1849_CR52","doi-asserted-by":"crossref","unstructured":"Wang, Q., Liu, L., Yu, W., Chen, S., Gong, J., Chen, P.: BCKD: block-correlation knowledge distillation. In: 2023 IEEE International Conference on Image Processing (ICIP), pp. 3225\u20133229. IEEE (2023)","DOI":"10.1109\/ICIP49359.2023.10222195"},{"key":"1849_CR53","doi-asserted-by":"crossref","unstructured":"Shu, C., Liu, Y., Gao, J., Yan, Z., Shen, C.: Channel-wise knowledge distillation for dense prediction. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 5311\u20135320 (2021)","DOI":"10.1109\/ICCV48922.2021.00526"},{"issue":"18","key":"1849_CR54","doi-asserted-by":"publisher","first-page":"1971","DOI":"10.3390\/agriculture15181971","volume":"15","author":"G Peng","year":"2025","unstructured":"Peng, G., Wang, K., Ma, J., Cui, B., Wang, D.: AGRI-YOLO: a lightweight model for corn weed detection with enhanced YOLO v11n. Agriculture 15(18), 1971 (2025)","journal-title":"Agriculture"},{"key":"1849_CR55","doi-asserted-by":"publisher","first-page":"1561632","DOI":"10.3389\/fpls.2025.1561632","volume":"16","author":"H Zheng","year":"2025","unstructured":"Zheng, H., Liu, C., Zhong, L., Wang, J., Huang, J., Lin, F., Ma, X., Tan, S.: An android-smartphone application for rice panicle detection and rice growth stage recognition using a lightweight YOLO network. Front. Plant Sci. 16, 1561632 (2025)","journal-title":"Front. Plant Sci."},{"key":"1849_CR56","doi-asserted-by":"publisher","DOI":"10.1016\/j.jajp.2025.100339","volume":"12","author":"S Zhang","year":"2025","unstructured":"Zhang, S., Jin, X., Lou, Z., Wang, S., Lu, S., He, Y.: LSDF-Net: an efficient lightweight defect detection method for ultrasonic welding surfaces. J. Adv. Join. Process. 12, 100339 (2025). https:\/\/doi.org\/10.1016\/j.jajp.2025.100339","journal-title":"J. Adv. Join. Process."},{"key":"1849_CR57","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2025.117269","volume":"251","author":"X Wang","year":"2025","unstructured":"Wang, X., Wan, S., Li, Z., Chen, X., Zhang, B., Wang, Y.: ECL-TEAR: lightweight detection method for multiple types of belt tears. Measurement 251, 117269 (2025)","journal-title":"Measurement"},{"key":"1849_CR58","unstructured":"Zhou, M.: TensorRT Pro-YOLOv8. https:\/\/github.com\/Melody-Zhou\/tensorRT_Pro-YOLOv8. Accessed 31 July 2025"},{"issue":"3","key":"1849_CR59","doi-asserted-by":"publisher","DOI":"10.1088\/1361-665X\/ad287c","volume":"33","author":"H Liang","year":"2024","unstructured":"Liang, H., Fu, J., Li, W., Xia, D., Luo, L., Yu, M.: Structural design and multi-objective optimization of a novel asymmetric magnetorheological damper. Smart Mater. Struct. 33(3), 035041 (2024)","journal-title":"Smart Mater. Struct."},{"issue":"24","key":"1849_CR60","doi-asserted-by":"publisher","first-page":"8893","DOI":"10.3390\/ma15248893","volume":"15","author":"W Li","year":"2022","unstructured":"Li, W., Liang, H., Xia, D., Fu, J., Luo, L., Yu, M.: Research on current drive system of magnetorheological damper based on fuzzy PI control. Materials 15(24), 8893 (2022)","journal-title":"Materials"},{"issue":"1","key":"1849_CR61","doi-asserted-by":"publisher","first-page":"3442","DOI":"10.1038\/s41598-024-54152-3","volume":"14","author":"G Ji","year":"2024","unstructured":"Ji, G., Zhang, L., Cai, M., Meng, X., Du, Z., Ruan, J., Guan, S., Liu, Z.: Research on variable universe fuzzy PID control for semi-active suspension with CDC dampers based on dynamic adjustment functions. Sci. Rep. 14(1), 3442 (2024)","journal-title":"Sci. Rep."},{"issue":"2","key":"1849_CR62","doi-asserted-by":"publisher","first-page":"859","DOI":"10.1002\/asjc.2579","volume":"24","author":"C Yin","year":"2022","unstructured":"Yin, C., Zhao, D., Zhang, J., Wang, S., Xu, X., Sun, X., Shi, D.: Body height robust control of automotive air suspension system using finite-time approach. Asian J. Control 24(2), 859\u2013871 (2022)","journal-title":"Asian J. Control"}],"container-title":["Journal of Real-Time Image Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11554-025-01849-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11554-025-01849-x","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11554-025-01849-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T16:51:28Z","timestamp":1770396688000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11554-025-01849-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,18]]},"references-count":62,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,2]]}},"alternative-id":["1849"],"URL":"https:\/\/doi.org\/10.1007\/s11554-025-01849-x","relation":{},"ISSN":["1861-8200","1861-8219"],"issn-type":[{"value":"1861-8200","type":"print"},{"value":"1861-8219","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,18]]},"assertion":[{"value":"11 October 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 December 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 January 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 conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"52"}}