{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T18:12:53Z","timestamp":1773943973912,"version":"3.50.1"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2025,4,28]],"date-time":"2025-04-28T00:00:00Z","timestamp":1745798400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,4,28]],"date-time":"2025-04-28T00:00:00Z","timestamp":1745798400000},"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":["Appl Intell"],"published-print":{"date-parts":[[2025,7]]},"DOI":"10.1007\/s10489-025-06582-3","type":"journal-article","created":{"date-parts":[[2025,4,28]],"date-time":"2025-04-28T05:15:33Z","timestamp":1745817333000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["EPDNet: Light-weight small target detection algorithm based on pruning and logical distillation"],"prefix":"10.1007","volume":"55","author":[{"given":"Gaofeng","family":"Zhu","sequence":"first","affiliation":[]},{"given":"Zhixue","family":"Wang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2886-6968","authenticated-orcid":false,"given":"Fenghua","family":"Zhu","sequence":"additional","affiliation":[]},{"given":"Gang","family":"Xiong","sequence":"additional","affiliation":[]},{"given":"Zheng","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,4,28]]},"reference":[{"issue":"3","key":"6582_CR1","doi-asserted-by":"publisher","first-page":"1787","DOI":"10.1007\/s00371-023-02886-y","volume":"40","author":"S Zeng","year":"2024","unstructured":"Zeng S, Yang W, Jiao Y, Geng L, Chen X (2024) Sca-yolo: a new small object detection model for uav images. Vis Comput 40(3):1787\u20131803","journal-title":"Vis Comput"},{"issue":"07","key":"6582_CR2","first-page":"157","volume":"47","author":"J Wong","year":"2024","unstructured":"Wong J, Cheng Y, Huang M, Sui H, Zhu H (2024) Small target detection in uav aerial images based on cs-yolov5s. Electron Meas Technol 47(07):157\u2013162","journal-title":"Electron Meas Technol"},{"key":"6582_CR3","doi-asserted-by":"crossref","unstructured":"Peng B, Niu K (2024) Research on intelligent oil drilling pipe column detection method based on improved lightweight target detection algorithm. IEEE Access","DOI":"10.1109\/ACCESS.2024.3362636"},{"issue":"1","key":"6582_CR4","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/s11554-023-01381-w","volume":"21","author":"L Qian","year":"2024","unstructured":"Qian L, Zheng Y, Cao J, Ma Y, Zhang Y, Liu X (2024) Lightweight ship target detection algorithm based on improved yolov5s. J Real-Time Image Process 21(1):3","journal-title":"J Real-Time Image Process"},{"key":"6582_CR5","doi-asserted-by":"crossref","unstructured":"Zhang Y, Ye M, Zhu G, Liu Y, Guo P, Yan J (2024) Ffca-yolo for small object detection in remote sensing images. IEEE Trans Geosci Remote Sens","DOI":"10.1109\/TGRS.2024.3363057"},{"key":"6582_CR6","doi-asserted-by":"crossref","unstructured":"Zhu G, Wang Z, Zhu F, Xiong G, Li Z, Shen G (2024) Small object recognition algorithm based on hybrid control and feature fusion. IEEE J Radio Freq Identif","DOI":"10.1109\/JRFID.2024.3384483"},{"key":"6582_CR7","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov D, Erhan D, Szegedy C, Reed S, Fu C-Y, Berg AC (2016) 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","DOI":"10.1007\/978-3-319-46448-0_2"},{"issue":"6","key":"6582_CR8","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 (2016) Faster r-cnn: towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell 39(6):1137\u20131149","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"6582_CR9","unstructured":"Wang L, Liu J, Wang W (2024) Small target detection method in uav images based on dilated convolution fusion transformer. J Comput Appl 0"},{"issue":"5","key":"6582_CR10","doi-asserted-by":"publisher","first-page":"304","DOI":"10.3390\/drones7050304","volume":"7","author":"Y Li","year":"2023","unstructured":"Li Y, Fan Q, Huang H, Han Z, Gu Q (2023) A modified yolov8 detection network for uav aerial image recognition. Drones 7(5):304","journal-title":"Drones"},{"key":"6582_CR11","doi-asserted-by":"crossref","unstructured":"Jiang X-N, Niu X-Q, Wu F-L, Fu Y, Bao H, Fan Y-C, Zhang Y, Pei J-Y (2025) A fine-grained aircraft target recognition algorithm for remote sensing images based on yolov8. IEEE J Sel Top Appl Earth Observ Remote Sens","DOI":"10.1109\/JSTARS.2025.3526982"},{"key":"6582_CR12","doi-asserted-by":"crossref","unstructured":"Jing W, Zhu Z, Chen H, Wang H, Shao F (2025) Towards large-scale non-motorized vehicle helmet wearing detection: a new benchmark and beyond. IEEE Trans Consum Electron","DOI":"10.1109\/TCE.2025.3527678"},{"key":"6582_CR13","doi-asserted-by":"crossref","unstructured":"Yu C, Liu Y, Zhao J, Shi Z (2024) Lr-net: a lightweight and robust network for infrared small target detection. arXiv preprint","DOI":"10.1007\/978-3-031-88223-4_2"},{"issue":"6","key":"6582_CR14","doi-asserted-by":"publisher","first-page":"240","DOI":"10.3390\/drones8060240","volume":"8","author":"S Wang","year":"2024","unstructured":"Wang S, Jiang H, Li Z, Yang J, Ma X, Chen J, Tang X (2024) Phsi-rtdetr: a lightweight infrared small target detection algorithm based on uav aerial photography. Drones 8(6):240","journal-title":"Drones"},{"issue":"2","key":"6582_CR15","doi-asserted-by":"publisher","first-page":"1427","DOI":"10.1007\/s13369-022-06874-7","volume":"48","author":"Y Chen","year":"2023","unstructured":"Chen Y, Zheng W, Zhao Y, Song TH, Shin H (2023) Dw-yolo: an efficient object detector for drones and self-driving vehicles. Arab J Sci Eng 48(2):1427\u20131436","journal-title":"Arab J Sci Eng"},{"key":"6582_CR16","doi-asserted-by":"crossref","unstructured":"Yu J, Chen J, Wan H, Zhou Z, Cao Y, Huang Z, Li Y, Wu B, Yao B (2024) Sargap: a full-link general decoupling automatic pruning algorithm for deep learning-based sar target detectors. IEEE Trans Geosci Remote Sens","DOI":"10.1109\/TGRS.2024.3350712"},{"issue":"04","key":"6582_CR17","first-page":"712","volume":"53","author":"Q Huang","year":"2024","unstructured":"Huang Q, Jin G, Xiong X, Wang M, Li J (2024) Lightweight sar target detection combining channel pruning and knowledge distillation. Acta Geodaet Cartographica Sin 53(04):712\u2013723","journal-title":"Acta Geodaet Cartographica Sin"},{"key":"6582_CR18","doi-asserted-by":"publisher","first-page":"8386","DOI":"10.1109\/JSTARS.2021.3104267","volume":"14","author":"Z Wang","year":"2021","unstructured":"Wang Z, Du L, Li Y (2021) Boosting lightweight cnns through network pruning and knowledge distillation for sar target recognition. IEEE J Sel Top Appl Earth Observ Remote Sens 14:8386\u20138397","journal-title":"IEEE J Sel Top Appl Earth Observ Remote Sens"},{"issue":"11","key":"6582_CR19","doi-asserted-by":"publisher","first-page":"2026","DOI":"10.3390\/rs16112026","volume":"16","author":"Q Ran","year":"2024","unstructured":"Ran Q, Li M, Zhao B, He Z, Wu Y (2024) L1rr: model pruning using dynamic and self-adaptive sparsity for remote-sensing target detection to prevent target feature loss. Remote Sens 16(11):2026","journal-title":"Remote Sens"},{"key":"6582_CR20","doi-asserted-by":"crossref","unstructured":"Bucilu\u01ce C, Caruana R, Niculescu-Mizil A (2006) Model compression. In: Proceedings of the 12th ACM SIGKDD international conference on knowledge discovery and data mining, pp 535\u2013541","DOI":"10.1145\/1150402.1150464"},{"key":"6582_CR21","unstructured":"Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. ArXiv"},{"key":"6582_CR22","unstructured":"Romero A, Ballas N, Kahou SE, Chassang A, Gatta C, Bengio Y (2014) Fitnets: hints for thin deep nets. ArXiv"},{"key":"6582_CR23","doi-asserted-by":"crossref","unstructured":"Fan Q, Huang H, Chen M, Liu H, He R (2024) Rmt: Retentive networks meet vision transformers. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 5641\u20135651","DOI":"10.1109\/CVPR52733.2024.00539"},{"issue":"2","key":"6582_CR24","doi-asserted-by":"publisher","first-page":"487","DOI":"10.1109\/JAS.2023.124029","volume":"11","author":"N Zeng","year":"2024","unstructured":"Zeng N, Li X, Wu P, Li H, Luo X (2024) A novel tensor decomposition-based efficient detector for low-altitude aerial objects with knowledge distillation scheme. IEEE\/CAA J Autom Sin 11(2):487\u2013501","journal-title":"IEEE\/CAA J Autom Sin"},{"key":"6582_CR25","doi-asserted-by":"crossref","unstructured":"Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, Xie S (2023) 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","DOI":"10.1109\/CVPR52729.2023.01548"},{"key":"6582_CR26","doi-asserted-by":"crossref","unstructured":"Ouyang D, He S, Zhang G, Luo M, Guo H, Zhan J, Huang Z (2023) Efficient multi-scale attention module with cross-spatial learning. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp 1\u20135. IEEE","DOI":"10.1109\/ICASSP49357.2023.10096516"},{"key":"6582_CR27","doi-asserted-by":"publisher","first-page":"111035","DOI":"10.1016\/j.asoc.2023.111035","volume":"150","author":"Q Zheng","year":"2024","unstructured":"Zheng Q, Xu L, Wang F, Xu Y, Lin C, Zhang G (2024) Hilbertscnet: self-attention networks for small target segmentation of aerial drone images. Appl Soft Comput 150:111035","journal-title":"Appl Soft Comput"},{"key":"6582_CR28","unstructured":"Lee J, Park S, Mo S, Ahn S, Shin J (2020) Layer-adaptive sparsity for the magnitude-based pruning. In: International conference on learning representations"},{"key":"6582_CR29","doi-asserted-by":"crossref","unstructured":"Yang L, Zhou X, Li X, Qiao L, Li Z, Yang Z, Wang G, Li X (2023) Bridging cross-task protocol inconsistency for distillation in dense object detection. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 17175\u201317184","DOI":"10.1109\/ICCV51070.2023.01575"},{"key":"6582_CR30","doi-asserted-by":"crossref","unstructured":"Du D, Zhu P, Wen L, Bian X, Lin H, Hu Q, Peng T, Zheng J, Wang X, Zhang Y et al (2019) Visdrone-det2019: The vision meets drone object detection in image challenge results. In: Proceedings of the IEEE\/CVF international conference on computer vision workshops, pp 0\u20130","DOI":"10.1109\/ICCVW.2019.00031"},{"issue":"3","key":"6582_CR31","doi-asserted-by":"publisher","first-page":"188","DOI":"10.3390\/drones7030188","volume":"7","author":"L Zhao","year":"2023","unstructured":"Zhao L, Zhu M (2023) Ms-yolov7: Yolov7 based on multi-scale for object detection on uav aerial photography. Drones 7(3):188","journal-title":"Drones"},{"key":"6582_CR32","unstructured":"Jinyi F, Zijia Z, Wei S, Kaixin Z (2024) Improved yolov8 small target detection algorithm in aerial images. J Comput Eng Appl 60(6)"},{"issue":"6","key":"6582_CR33","doi-asserted-by":"publisher","first-page":"066003","DOI":"10.1088\/1402-4896\/ad418f","volume":"99","author":"H Lou","year":"2024","unstructured":"Lou H, Liu X, Bi L, Liu H, Guo J (2024) Bd-yolo: detection algorithm for high-resolution remote sensing images. Phys Scr 99(6):066003","journal-title":"Phys Scr"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-025-06582-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-025-06582-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-025-06582-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T13:57:07Z","timestamp":1758290227000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-025-06582-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,28]]},"references-count":33,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2025,7]]}},"alternative-id":["6582"],"URL":"https:\/\/doi.org\/10.1007\/s10489-025-06582-3","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,28]]},"assertion":[{"value":"16 April 2025","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 April 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 no competing interests related to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}},{"value":"All the data utilized in this study were sourced from authorized, publicly accessible platforms and adhered to privacy and intellectual property regulations. To protect personal privacy, we implemented robust anonymization and de-identification protocols during data handling. We are open to sharing the data, provided that it aligns with applicable laws, regulations, and ethical standards. We assure that the research process will fully comply with ethical principles and maintain moral integrity.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical and Informed Consent for Data Used Statement"}}],"article-number":"699"}}