{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,7]],"date-time":"2026-02-07T10:50:58Z","timestamp":1770461458567,"version":"3.49.0"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,12,16]],"date-time":"2025-12-16T00:00:00Z","timestamp":1765843200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,12,16]],"date-time":"2025-12-16T00:00:00Z","timestamp":1765843200000},"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":[[2026,2]]},"DOI":"10.1007\/s11554-025-01820-w","type":"journal-article","created":{"date-parts":[[2025,12,16]],"date-time":"2025-12-16T15:46:50Z","timestamp":1765900010000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Improved YOLO for fabric defect detection algorithm deployment based on FPGA"],"prefix":"10.1007","volume":"23","author":[{"given":"Haobin","family":"Xiang","sequence":"first","affiliation":[]},{"given":"Wang","family":"Zhen","sequence":"additional","affiliation":[]},{"given":"Zeliang","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Bingrui","family":"Li","sequence":"additional","affiliation":[]},{"given":"Chunlei","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,12,16]]},"reference":[{"issue":"3","key":"1820_CR1","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1007\/s41324-022-00494-x","volume":"31","author":"A Goel","year":"2023","unstructured":"Goel, A., Goel, A.K., Kumar, A.: The role of artificial neural network and machine learning in utilizing spatial information. Spat. Inf. Res. 31(3), 275\u2013285 (2023)","journal-title":"Spat. Inf. Res."},{"key":"1820_CR2","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":"1820_CR3","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":"1820_CR4","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":"1820_CR5","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":"1820_CR6","doi-asserted-by":"crossref","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, pp. 21\u201337. Springer (2016)","DOI":"10.1007\/978-3-319-46448-0_2"},{"issue":"22","key":"1820_CR7","doi-asserted-by":"publisher","first-page":"5853","DOI":"10.3390\/rs14225853","volume":"14","author":"L Shenglian","year":"2022","unstructured":"Shenglian, L., Liu, X., He, Z., Zhang, X., Liu, W., Karkee, M.: Swin-transformer-YOLOv5 for real-time wine grape bunch detection. Remote Sens. 14(22), 5853 (2022)","journal-title":"Remote Sens."},{"key":"1820_CR8","doi-asserted-by":"crossref","unstructured":"Zhu, X., Lyu, S., Wang, X., Zhao, Q.: TPH-YOLOv5: improved YOLOv5 based on transformer prediction head for object detection on drone-captured scenarios. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 2778\u20132788 (2021)","DOI":"10.1109\/ICCVW54120.2021.00312"},{"key":"1820_CR9","doi-asserted-by":"publisher","first-page":"805","DOI":"10.1007\/s00371-020-01831-7","volume":"37","author":"W Chen","year":"2021","unstructured":"Chen, W., Huang, H., Peng, S., Zhou, C., Zhang, C.: YOLO-face: a real-time face detector. Vis. Comput. 37, 805\u2013813 (2021)","journal-title":"Vis. Comput."},{"key":"1820_CR10","doi-asserted-by":"crossref","unstructured":"Sruthi, K., Nandakumar, R.: AI\/ML-based object detection on FPGA SOC. In: International Conference on Communication, Electronics and Digital Technology, pp. 479\u2013487. Springer (2023)","DOI":"10.1007\/978-981-99-1699-3_32"},{"issue":"3","key":"1820_CR11","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1007\/s40009-023-01344-6","volume":"47","author":"A Kumar","year":"2024","unstructured":"Kumar, A.: Bit plane slicing chip using parallel processing in image processing. Natl. Acad. Sci. Lett. 47(3), 261\u2013266 (2024)","journal-title":"Natl. Acad. Sci. Lett."},{"issue":"18","key":"1820_CR12","doi-asserted-by":"publisher","first-page":"28213","DOI":"10.1007\/s11042-023-14627-3","volume":"82","author":"A Goel","year":"2023","unstructured":"Goel, A., Goel, A.K., Kumar, A.: Performance analysis of multiple input single layer neural network hardware chip. Multimedia Tools Appl. 82(18), 28213\u201328234 (2023)","journal-title":"Multimedia Tools Appl."},{"key":"1820_CR13","doi-asserted-by":"publisher","DOI":"10.1007\/s40009-025-01670-x","author":"A Goel","year":"2025","unstructured":"Goel, A., Katiyar, A., Goel, A.K., Kumar, A.: Comparative study of ANN, CNN, and RNN hardware chips. Natl. Acad. Sci. Lett. (2025). https:\/\/doi.org\/10.1007\/s40009-025-01670-x","journal-title":"Natl. Acad. Sci. Lett."},{"issue":"4","key":"1820_CR14","doi-asserted-by":"publisher","first-page":"495","DOI":"10.11591\/ijra.v13i4.pp495-505","volume":"13","author":"A Pant","year":"2024","unstructured":"Pant, A., Kumar, A.: Design and implementation of deep neural network hardware chip and its performance analysis. IAES Int. J. Robot. Autom. (IJRA) 13(4), 495\u2013505 (2024)","journal-title":"IAES Int. J. Robot. Autom. (IJRA)"},{"key":"1820_CR15","doi-asserted-by":"crossref","unstructured":"Zhang, J., Zhang, F., Xie, M., Liu, X., Feng, T.: Design and implementation of CNN traffic lights classification based on FPGA. In: 2021 IEEE 4th International Conference on Electronic Information and Communication Technology (ICEICT), pp. 445\u2013449. IEEE (2021)","DOI":"10.1109\/ICEICT53123.2021.9531169"},{"issue":"8","key":"1820_CR16","doi-asserted-by":"publisher","first-page":"1861","DOI":"10.1109\/TVLSI.2019.2905242","volume":"27","author":"DT Nguyen","year":"2019","unstructured":"Nguyen, D.T., Nguyen, T.N., Kim, H., Lee, H.-J.: A high-throughput and power-efficient FPGA implementation of YOLO CNN for object detection. IEEE Trans. Very Large Scale Integr. (VLSI) Syst. 27(8), 1861\u20131873 (2019)","journal-title":"IEEE Trans. Very Large Scale Integr. (VLSI) Syst."},{"issue":"3","key":"1820_CR17","doi-asserted-by":"publisher","first-page":"282","DOI":"10.3390\/electronics10030282","volume":"10","author":"N Zhang","year":"2021","unstructured":"Zhang, N., Wei, X., Chen, H., Liu, W.: FPGA implementation for CNN-based optical remote sensing object detection. Electronics 10(3), 282 (2021)","journal-title":"Electronics"},{"issue":"6","key":"1820_CR18","doi-asserted-by":"publisher","first-page":"279","DOI":"10.3390\/info13060279","volume":"13","author":"S Kalapothas","year":"2022","unstructured":"Kalapothas, S., Flamis, G., Kitsos, P.: Efficient edge-AI application deployment for FPGAS. Information 13(6), 279 (2022)","journal-title":"Information"},{"issue":"3","key":"1820_CR19","doi-asserted-by":"publisher","first-page":"194","DOI":"10.3390\/info14030194","volume":"14","author":"K Shi","year":"2023","unstructured":"Shi, K., Wang, M., Tan, X., Li, Q., Lei, T.: Efficient dynamic reconfigurable CNN accelerator for edge intelligence computing on FPGA. Information 14(3), 194 (2023)","journal-title":"Information"},{"key":"1820_CR20","doi-asserted-by":"publisher","first-page":"131788","DOI":"10.1109\/ACCESS.2022.3229767","volume":"10","author":"P Dhilleswararao","year":"2022","unstructured":"Dhilleswararao, P., Srinivas Boppu, M., Manikandan, S., Cenkeramaddi, L.R.: Efficient hardware architectures for accelerating deep neural networks: survey. IEEE Access 10, 131788\u2013131828 (2022)","journal-title":"IEEE Access"},{"key":"1820_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2022.105604","volume":"117","author":"SC Magalh\u00e3es","year":"2023","unstructured":"Magalh\u00e3es, S.C., dos Santos, F., Neves, M., Pedro, M., Paulo, A., Dias, J.: Benchmarking edge computing devices for grape bunches and trunks detection using accelerated object detection single shot multibox deep learning models. Eng. Appl. Artif. Intell. 117, 105604 (2023)","journal-title":"Eng. Appl. Artif. Intell."},{"key":"1820_CR22","doi-asserted-by":"crossref","unstructured":"Baczmanski, M., Wasala, M., Kryjak, T.: Implementation of a perception system for autonomous vehicles using a detection-segmentation network in SOC FPGA. In: International Symposium on Applied Reconfigurable Computing, pp. 200\u2013211. Springer (2023)","DOI":"10.1007\/978-3-031-42921-7_14"},{"key":"1820_CR23","doi-asserted-by":"crossref","unstructured":"Tran, T.H.-P., Jeon, J.W.: Accurate real-time traffic light detection using YOLOv4. In: 2020 IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia), pp. 1\u20134. IEEE (2020)","DOI":"10.1109\/ICCE-Asia49877.2020.9277063"},{"key":"1820_CR24","doi-asserted-by":"crossref","unstructured":"Wu, S., Amenta, N., Zhou, J., Papais, S., Kelly, J.: aUToLights: a robust multi-camera traffic light detection and tracking system. In: 2023 20th Conference on Robots and Vision (CRV), pp. 89\u201396. IEEE (2023)","DOI":"10.1109\/CRV60082.2023.00019"},{"key":"1820_CR25","doi-asserted-by":"crossref","unstructured":"Abraham, A., Purwanto, D., Kusuma, H.: Traffic lights and traffic signs detection system using modified you only look once. In: 2021 International seminar on intelligent technology and its applications (ISITIA), pp. 141\u2013146. IEEE (2021)","DOI":"10.1109\/ISITIA52817.2021.9502268"},{"key":"1820_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.mejo.2023.105805","volume":"137","author":"Yu Wang","year":"2023","unstructured":"Wang, Yu., Liao, Y., Yang, J., Wang, H., Zhao, Y., Zhang, C., Xiao, B., Fei, X., Gao, Y., Mingzhu, X., et al.: An FPGA-based online reconfigurable CNN edge computing device for object detection. Microelectron. J. 137, 105805 (2023)","journal-title":"Microelectron. J."},{"issue":"21","key":"1820_CR27","doi-asserted-by":"publisher","first-page":"21357","DOI":"10.1109\/JIOT.2022.3179016","volume":"9","author":"MA Zhichao Zhang","year":"2022","unstructured":"Zhichao Zhang, M.A., Mahmud, P., Kouzani, A.Z.: FITNN: a low-resource FPGA-based CNN accelerator for drones. IEEE Internet Things J. 9(21), 21357\u201321369 (2022)","journal-title":"IEEE Internet Things J."},{"key":"1820_CR28","doi-asserted-by":"crossref","unstructured":"Heller, D., Rizk, M., Douguet, R., Baghdadi, A., Diguet, J.-Ph.: Marine objects detection using deep learning on embedded edge devices. In: 2022 IEEE International Workshop on Rapid System Prototyping (RSP), pp. 1\u20137. IEEE (2022)","DOI":"10.1109\/RSP57251.2022.10039025"},{"key":"1820_CR29","doi-asserted-by":"crossref","unstructured":"Montgomerie-Corcoran, A., Toupas, P., Yu, Z., Bouganis, C.-S.: SATAY: a streaming architecture toolflow for accelerating YOLO models on FPGA devices. In: 2023 International Conference on Field Programmable Technology (ICFPT), pp. 179\u2013187. IEEE (2023)","DOI":"10.1109\/ICFPT59805.2023.00025"},{"issue":"3","key":"1820_CR30","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1007\/s11554-024-01440-w","volume":"21","author":"D-D Nguyen","year":"2024","unstructured":"Nguyen, D.-D., Nguyen, D.-T., Le, M.-T., Nguyen, Q.-C.: FPGA-SOC implementation of YOLOv4 for flying-object detection. J. Real-Time Image Proc. 21(3), 63 (2024)","journal-title":"J. Real-Time Image Proc."},{"issue":"5","key":"1820_CR31","doi-asserted-by":"publisher","first-page":"6699","DOI":"10.1007\/s11227-023-05713-2","volume":"80","author":"Z Valadanzoj","year":"2024","unstructured":"Valadanzoj, Z., Daryanavard, H., Harifi, A.: High-speed YOLOv4-tiny hardware accelerator for self-driving automotive. J. Supercomput. 80(5), 6699\u20136724 (2024)","journal-title":"J. Supercomput."},{"key":"1820_CR32","unstructured":"Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 (2017)"},{"key":"1820_CR33","first-page":"12993","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. Proc. AAAI Conf. Artif. Intell. 34, 12993\u201313000 (2020)","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"1820_CR34","unstructured":"Gevorgyan, Z.: SIoU loss: more powerful learning for bounding box regression. arxiv 2022. arXiv preprint arXiv:2205.12740 (2022)"},{"key":"1820_CR35","doi-asserted-by":"publisher","first-page":"276","DOI":"10.1016\/j.neunet.2023.11.041","volume":"170","author":"C Liu","year":"2024","unstructured":"Liu, C., Wang, K., Li, Q., Zhao, F., Zhao, K., Ma, H.: Powerful-IoU: more straightforward and faster bounding box regression loss with a nonmonotonic focusing mechanism. Neural Netw. 170, 276\u2013284 (2024)","journal-title":"Neural Netw."},{"key":"1820_CR36","doi-asserted-by":"crossref","unstructured":"Rathod, G., Shah, P., Gajjar, R., Patel, M.I., Gajjar, N.: Implementation of real-time object detection on FPGA. In: 2023 7th International Conference on Trends in Electronics and Informatics (ICOEI), pp. 235\u2013240. IEEE (2023)","DOI":"10.1109\/ICOEI56765.2023.10125958"},{"key":"1820_CR37","doi-asserted-by":"crossref","unstructured":"Jiang, B., Xie, X., Yi, L.: Improved YOLOv5s algorithm for aluminum sheet surface defect detection deployed on FPGA. In: Proceedings of the 2024 8th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence, pp. 49\u201356 (2024)","DOI":"10.1145\/3665065.3665074"},{"key":"1820_CR38","doi-asserted-by":"publisher","DOI":"10.1088\/1742-6596\/2732\/1\/012013","volume":"2732","author":"J Liu","year":"2024","unstructured":"Liu, J., Mao, M., Gao, J., Bai, J., Sun, D.: Hardware-accelerated YOLOv5 based on MPSOC. J. Phys. Conf. Ser. 2732, 012013 (2024)","journal-title":"J. Phys. Conf. Ser."},{"issue":"9","key":"1820_CR39","doi-asserted-by":"publisher","first-page":"1164","DOI":"10.3390\/mi15091164","volume":"15","author":"Z Yan","year":"2024","unstructured":"Yan, Z., Zhang, B., Wang, D.: An FPGA-based YOLOv5 accelerator for real-time industrial vision applications. Micromachines 15(9), 1164 (2024)","journal-title":"Micromachines"},{"key":"1820_CR40","doi-asserted-by":"publisher","first-page":"73268","DOI":"10.1109\/ACCESS.2024.3404623","volume":"12","author":"R Al Amin","year":"2024","unstructured":"Al Amin, R., Hasan, M., Wiese, V., Obermaisser, R.: FPGA-based real-time object detection and classification system using YOLO for edge computing. IEEE Access 12, 73268\u201373278 (2024)","journal-title":"IEEE Access"}],"container-title":["Journal of Real-Time Image Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11554-025-01820-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11554-025-01820-w","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11554-025-01820-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T16:48:24Z","timestamp":1770396504000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11554-025-01820-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,16]]},"references-count":40,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,2]]}},"alternative-id":["1820"],"URL":"https:\/\/doi.org\/10.1007\/s11554-025-01820-w","relation":{},"ISSN":["1861-8200","1861-8219"],"issn-type":[{"value":"1861-8200","type":"print"},{"value":"1861-8219","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,16]]},"assertion":[{"value":"7 December 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 November 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 December 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":"31"}}