{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T04:53:47Z","timestamp":1777438427700,"version":"3.51.4"},"reference-count":28,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,11,25]],"date-time":"2024-11-25T00:00:00Z","timestamp":1732492800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,25]],"date-time":"2024-11-25T00:00:00Z","timestamp":1732492800000},"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":[[2025,2]]},"DOI":"10.1007\/s11554-024-01582-x","type":"journal-article","created":{"date-parts":[[2024,11,25]],"date-time":"2024-11-25T04:53:45Z","timestamp":1732510425000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["Optimized YOLOv8 for multi-scale object detection"],"prefix":"10.1007","volume":"22","author":[{"given":"Areeg Fahad","family":"Rasheed","sequence":"first","affiliation":[]},{"given":"M.","family":"Zarkoosh","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,11,25]]},"reference":[{"key":"1582_CR1","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1109\/JPROC.2023.3238524","volume":"111","author":"Z Zou","year":"2023","unstructured":"Zou, Z., Chen, K., Shi, Z., Guo, Y., Ye, J.: Object detection in 20 years: a survey. Proc. IEEE 111, 257\u2013276 (2023)","journal-title":"Proc. IEEE"},{"key":"1582_CR2","doi-asserted-by":"publisher","first-page":"1039","DOI":"10.24996\/ijs.2024.65.2.35","volume":"2024","author":"AH Alrubaie","year":"2024","unstructured":"Alrubaie, A.H., Abdulameer, A.T., et al.: Improving the yolov7 algorithm for object detection within recorded videos. Iraqi J. Sci. 2024, 1039\u20131047 (2024)","journal-title":"Iraqi J. Sci."},{"key":"1582_CR3","doi-asserted-by":"publisher","first-page":"2533","DOI":"10.24996\/ijs.2023.64.5.36","volume":"64","author":"D Shaker","year":"2023","unstructured":"Shaker, D., Abbas, A.: Material recognition of foreign object debris using deep learning. Iraqi J. Sci. 64, 2533\u20132544 (2023)","journal-title":"Iraqi J. Sci."},{"key":"1582_CR4","doi-asserted-by":"publisher","first-page":"15","DOI":"10.33640\/2405-609X.3298","volume":"9","author":"IS Razaq","year":"2023","unstructured":"Razaq, I.S., et al.: Improved face morphing attack detection method using PCA and convolutional neural network. Karbala Int. J. Mod. Sci. 9, 15 (2023)","journal-title":"Karbala Int. J. Mod. Sci."},{"key":"1582_CR5","first-page":"1","volume":"2021","author":"A Kaur","year":"2021","unstructured":"Kaur, A., Singh, Y., Neeru, N., Kaur, L., Singh, A.: A survey on deep learning approaches to medical images and a systematic look up into real-time object detection. Arch. Comput. Methods Eng. 2021, 1\u201341 (2021)","journal-title":"Arch. Comput. Methods Eng."},{"key":"1582_CR6","first-page":"9090","volume":"73","author":"H Wang","year":"2024","unstructured":"Wang, H., Liu, C., Cai, Y., Chen, L., Li, Y.: YOLOv8-QSD: an improved small object detection algorithm for autonomous vehicles based on YOLOv8. IEEE Trans. Instrum. Meas. 73, 9090 (2024)","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"1582_CR7","first-page":"11","volume":"7","author":"M Talib","year":"2024","unstructured":"Talib, M., Saud, J.H.: A multi-weapon detection using deep learning. Iraqi J. Inf. Commun. Technol. 7, 11\u201322 (2024)","journal-title":"Iraqi J. Inf. Commun. Technol."},{"key":"1582_CR8","doi-asserted-by":"crossref","unstructured":"Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection, pp. 886\u2013893 (2005)","DOI":"10.1109\/CVPR.2005.177"},{"key":"1582_CR9","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, vol. 25 (2012)"},{"key":"1582_CR10","doi-asserted-by":"publisher","DOI":"10.1088\/1742-6596\/1544\/1\/012033","volume":"1544","author":"L Du","year":"2020","unstructured":"Du, L., Zhang, R., Wang, X.: Overview of two-stage object detection algorithms. J. Phys. Conf. Ser. 1544, 012033 (2020)","journal-title":"J. Phys. Conf. Ser."},{"key":"1582_CR11","doi-asserted-by":"crossref","unstructured":"Girshick, R.: Fast r-cnn (2015). arXiv preprint arXiv:1504.08083","DOI":"10.1109\/ICCV.2015.169"},{"key":"1582_CR12","unstructured":"Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, vol. 28 (2015)"},{"key":"1582_CR13","first-page":"12","volume":"4","author":"AA Aggar","year":"2021","unstructured":"Aggar, A.A., Zaiter, M.J., Raheem, A.T.: Real object detection system for Iraq traffic signs based on mask r-cnn. Iraqi J. Inf. Commun. Technol. 4, 12\u201322 (2021)","journal-title":"Iraqi J. Inf. Commun. Technol."},{"key":"1582_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2023.109377","volume":"138","author":"C Wang","year":"2023","unstructured":"Wang, C., Wang, H.: Cascaded feature fusion with multi-level self-attention mechanism for object detection. Pattern Recognit. 138, 109377 (2023)","journal-title":"Pattern Recognit."},{"key":"1582_CR15","doi-asserted-by":"publisher","first-page":"930","DOI":"10.3390\/pr12050930","volume":"12","author":"M Liu","year":"2024","unstructured":"Liu, M., Zhang, M., Chen, X., Zheng, C., Wang, H.: YOLOv8-LMG: an improved bearing defect detection algorithm based on YOLOv8. Processes 12, 930 (2024)","journal-title":"Processes"},{"key":"1582_CR16","first-page":"15","volume":"2020","author":"MM Saleh","year":"2020","unstructured":"Saleh, M.M.: WSNs and IoT their challenges and applications for healthcare and agriculture: a survey. Iraqi J. Electr. Electron. Eng. 2020, 15\u201316 (2020)","journal-title":"Iraqi J. Electr. Electron. Eng."},{"key":"1582_CR17","doi-asserted-by":"crossref","unstructured":"Wang, C.-Y. et\u00a0al.: Cspnet: a new backbone that can enhance learning capability of cnn. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 1571\u20131580 (2019). https:\/\/api.semanticscholar.org\/CorpusID:208310312","DOI":"10.1109\/CVPRW50498.2020.00203"},{"key":"1582_CR18","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y. et\u00a0al.: Feature pyramid networks for object detection. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 936\u2013944 (2016). https:\/\/api.semanticscholar.org\/CorpusID:10716717","DOI":"10.1109\/CVPR.2017.106"},{"key":"1582_CR19","doi-asserted-by":"crossref","unstructured":"Liu, S., Qi, L., Qin, H., Shi, J., Jia, J.: Path aggregation network for instance segmentation. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8759\u20138768 (2018). https:\/\/api.semanticscholar.org\/CorpusID:3698141","DOI":"10.1109\/CVPR.2018.00913"},{"key":"1582_CR20","doi-asserted-by":"publisher","first-page":"1904","DOI":"10.1109\/TPAMI.2015.2389824","volume":"37","author":"K He","year":"2015","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. 37, 1904\u20131916 (2015)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1582_CR21","unstructured":"new-workspace csmgu: Weedcrop dataset (2022). https:\/\/universe.roboflow.com\/new-workspace-csmgu\/weedcrop-waifl. Visited on 2024-05-13"},{"key":"1582_CR22","unstructured":"Roboflow: Bccd dataset (2022). https:\/\/universe.roboflow.com\/joseph-nelson\/bccd. Visited on 2024-05-13"},{"key":"1582_CR23","unstructured":"100, R.: underwater pipes dataset (2023). https:\/\/universe.roboflow.com\/roboflow-100\/underwater-pipes-4ng4t. Visited on 2024-05-13"},{"key":"1582_CR24","unstructured":"GDIT: Aerial airport dataset (2024). https:\/\/universe.roboflow.com\/gdit\/aerial-airport. Visited on 2024-05-13"},{"key":"1582_CR25","unstructured":"mozartdados: animals dataset (2024). https:\/\/universe.roboflow.com\/mozartdados\/animals-ahty7. Visited on 2024-05-13"},{"key":"1582_CR26","unstructured":"face-for-small-large dataset (2024). https:\/\/universe.roboflow.com\/ok-4sjtq\/face-for-small-large. Visited on 2024-05-13"},{"key":"1582_CR27","doi-asserted-by":"crossref","unstructured":"Li, W., Guo, X., Yuan, Y.: Novel scenes & classes: towards adaptive open-set object detection, pp. 15780\u201315790 (2023)","DOI":"10.1109\/ICCV51070.2023.01446"},{"key":"1582_CR28","doi-asserted-by":"crossref","unstructured":"Li, W., Liu, X., Yuan, Y.: Sigma: semantic-complete graph matching for domain adaptive object detection, pp. 5291\u20135300 (2022)","DOI":"10.1109\/CVPR52688.2022.00522"}],"container-title":["Journal of Real-Time Image Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11554-024-01582-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11554-024-01582-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11554-024-01582-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,3]],"date-time":"2025-02-03T17:18:23Z","timestamp":1738603103000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11554-024-01582-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,25]]},"references-count":28,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,2]]}},"alternative-id":["1582"],"URL":"https:\/\/doi.org\/10.1007\/s11554-024-01582-x","relation":{},"ISSN":["1861-8200","1861-8219"],"issn-type":[{"value":"1861-8200","type":"print"},{"value":"1861-8219","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,25]]},"assertion":[{"value":"26 September 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 November 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 November 2024","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 that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}],"article-number":"6"}}