{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T19:09:57Z","timestamp":1776712197560,"version":"3.51.2"},"reference-count":77,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,12,4]],"date-time":"2024-12-04T00:00:00Z","timestamp":1733270400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2024,12,4]],"date-time":"2024-12-04T00:00:00Z","timestamp":1733270400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62176052"],"award-info":[{"award-number":["62176052"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007219","name":"Natural Science Foundation of Shanghai Municipality","doi-asserted-by":"publisher","award":["21ZR1401700"],"award-info":[{"award-number":["21ZR1401700"]}],"id":[{"id":"10.13039\/100007219","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Complex Intell. Syst."],"published-print":{"date-parts":[[2025,1]]},"DOI":"10.1007\/s40747-024-01676-w","type":"journal-article","created":{"date-parts":[[2024,12,4]],"date-time":"2024-12-04T13:41:36Z","timestamp":1733319696000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["A novel knowledge distillation framework for enhancing small object detection in blurry environments with unmanned aerial vehicle-assisted images"],"prefix":"10.1007","volume":"11","author":[{"given":"Sayed","family":"Jobaer","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7594-2241","authenticated-orcid":false,"given":"Xue-song","family":"Tang","sequence":"additional","affiliation":[]},{"given":"Yihong","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Gaojian","family":"Li","sequence":"additional","affiliation":[]},{"given":"Foysal","family":"Ahmed","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,12,4]]},"reference":[{"key":"1676_CR1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-91735-7_13","volume-title":"The handbook of security","author":"M Button","year":"2022","unstructured":"Button M, Knickmeier S (2022) Economic and industrial espionage: characteristics, techniques and response. In: Gill M (ed) The handbook of security. Palgrave Macmillan, Cham. https:\/\/doi.org\/10.1007\/978-3-030-91735-7_13"},{"key":"1676_CR2","doi-asserted-by":"publisher","DOI":"10.3389\/fnhum.2018.00374","author":"JG Martin","year":"2018","unstructured":"Martin JG, Davis CE, Riesenhuber M, Thorpe SJ (2018) High resolution human eye tracking during continuous visual search. Front Hum Neurosci. https:\/\/doi.org\/10.3389\/fnhum.2018.00374","journal-title":"Front Hum Neurosci"},{"key":"1676_CR3","doi-asserted-by":"crossref","unstructured":"Kitimbo A, Lucey A, Maru MT (2021) World migration report 2022 : chapter 6 - peace and security as drivers of stability, development and safe migration,\u00a0Geneva : International Organization for Migration, 2021Research Report,\u00a02021\/06,\u00a0World Migration Report. https:\/\/cadmus.eui.eu\/handle\/1814\/76616","DOI":"10.1002\/wom3.27"},{"key":"1676_CR4","doi-asserted-by":"publisher","first-page":"1245","DOI":"10.1016\/j.visres.2006.12.001","volume":"47","author":"KJ Ciuffreda","year":"2007","unstructured":"Ciuffreda KJ, Wang B, Vasudevan B (2007) Conceptual model of human blur perception. Vision Res 47:1245\u20131252","journal-title":"Vision Res"},{"issue":"5","key":"1676_CR5","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1167\/17.5.3","volume":"17","author":"G Maiello","year":"2017","unstructured":"Maiello G, Walker L, Bex PJ, Vera-Diaz FA (2017) Blur perception throughout the visual field in myopia and emmetropia. J Vis 17(5):3. https:\/\/doi.org\/10.1167\/17.5.3","journal-title":"J Vis"},{"key":"1676_CR6","doi-asserted-by":"publisher","DOI":"10.1523\/ENEURO.0443-17.2018","author":"M Abdelhack","year":"2018","unstructured":"Abdelhack M, Kamitani Y (2018) Sharpening of hierarchical visual feature representations of blurred images. eNeuro. https:\/\/doi.org\/10.1523\/ENEURO.0443-17.2018","journal-title":"eNeuro"},{"key":"1676_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/LGRS.2023.3323534","volume":"21","author":"T Lei","year":"2024","unstructured":"Lei T et al (2024) Lightweight structure-aware transformer network for remote sensing image change detection. IEEE Geosci Remote Sens Lett. 21:1\u20135. https:\/\/doi.org\/10.1109\/LGRS.2023.3323534","journal-title":"IEEE Geosci Remote Sens Lett."},{"issue":"5","key":"1676_CR8","doi-asserted-by":"publisher","first-page":"2720","DOI":"10.1109\/TCYB.2024.3357213","volume":"54","author":"G Xie","year":"2024","unstructured":"Xie G et al (2024) IM-IAD: industrial image anomaly detection benchmark in manufacturing. IEEE Trans Cybern 54(5):2720\u20132733. https:\/\/doi.org\/10.1109\/TCYB.2024.3357213","journal-title":"IEEE Trans Cybern"},{"key":"1676_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.jai.2023.10.002","author":"J Liu","year":"2023","unstructured":"Liu J, Jin Y (2023) A comprehensive survey of robust deep learning in computer vision. J Automat Intell. https:\/\/doi.org\/10.1016\/j.jai.2023.10.002","journal-title":"J Automat Intell"},{"key":"1676_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2023.3261273","volume":"61","author":"T Lei","year":"2023","unstructured":"Lei T et al (2023) Ultralightweight spatial-spectral feature cooperation network for change detection in remote sensing images. IEEE Trans Geosci Remote Sens 61:1\u201314. https:\/\/doi.org\/10.1109\/TGRS.2023.3261273","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"1676_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2023.3320288","volume":"61","author":"D Xue","year":"2023","unstructured":"Xue D et al (2023) Triple change detection network via joint multifrequency and full-scale swin-transformer for remote sensing images. IEEE Trans Geosci Remote Sens 61:1\u201315. https:\/\/doi.org\/10.1109\/TGRS.2023.3320288","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"1676_CR12","doi-asserted-by":"publisher","first-page":"6283","DOI":"10.1007\/s00521-024-09422-6","volume":"36","author":"W Wei","year":"2024","unstructured":"Wei W, Cheng Y, He J et al (2024) A review of small object detection based on deep learning. Neural Comput Applic 36:6283\u20136303. https:\/\/doi.org\/10.1007\/s00521-024-09422-6","journal-title":"Neural Comput Applic"},{"key":"1676_CR13","doi-asserted-by":"publisher","first-page":"5459","DOI":"10.1007\/s40747-024-01448-6","volume":"10","author":"M Li","year":"2024","unstructured":"Li M, Chen Y, Zhang T et al (2024) TA-YOLO: a lightweight small object detection model based on multi-dimensional trans-attention module for remote sensing images. Complex Intell Syst 10:5459\u20135473. https:\/\/doi.org\/10.1007\/s40747-024-01448-6","journal-title":"Complex Intell Syst"},{"key":"1676_CR14","doi-asserted-by":"publisher","DOI":"10.1007\/s40747-024-01512-1","author":"X Hu","year":"2024","unstructured":"Hu X, Lin S (2024) DFFNet: a lightweight approach for efficient feature-optimized fusion in steel strip surface defect detection. Complex Intell Syst. https:\/\/doi.org\/10.1007\/s40747-024-01512-1","journal-title":"Complex Intell Syst"},{"key":"1676_CR15","doi-asserted-by":"publisher","first-page":"2083","DOI":"10.1007\/s40747-023-01256-4","volume":"10","author":"Y Wan","year":"2024","unstructured":"Wan Y, Li J (2024) LGP-YOLO: an efficient convolutional neural network for surface defect detection of light guide plate. Complex Intell Syst 10:2083\u20132105. https:\/\/doi.org\/10.1007\/s40747-023-01256-4","journal-title":"Complex Intell Syst"},{"key":"1676_CR16","doi-asserted-by":"publisher","first-page":"3881","DOI":"10.1007\/s40747-022-00683-z","volume":"8","author":"X Wang","year":"2022","unstructured":"Wang X, Liu J, Liu X et al (2022) Ship feature recognition methods for deep learning in complex marine environments. Complex Intell Syst 8:3881\u20133897. https:\/\/doi.org\/10.1007\/s40747-022-00683-z","journal-title":"Complex Intell Syst"},{"key":"1676_CR17","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1007\/s40747-022-00786-7","volume":"9","author":"X Li","year":"2023","unstructured":"Li X, He M, Liu Y et al (2023) SPCS: a spatial pyramid convolutional shuffle module for YOLO to detect occluded object. Complex Intell Syst 9:301\u2013315. https:\/\/doi.org\/10.1007\/s40747-022-00786-7","journal-title":"Complex Intell Syst"},{"key":"1676_CR18","doi-asserted-by":"publisher","first-page":"6343","DOI":"10.1007\/s40747-023-01072-w","volume":"9","author":"Y Zhang","year":"2023","unstructured":"Zhang Y, Zhang Z, Zhang P et al (2023) Salient object detection for RGBD video via spatial interaction and depth-based boundary refinement. Complex Intell Syst 9:6343\u20136358. https:\/\/doi.org\/10.1007\/s40747-023-01072-w","journal-title":"Complex Intell Syst"},{"key":"1676_CR19","doi-asserted-by":"publisher","DOI":"10.1007\/s40747-024-01458-4","author":"W Xie","year":"2024","unstructured":"Xie W, Zeng Y (2024) A knowledge distillation based cross-modal learning framework for the lithium-ion battery state of health estimation. Complex Intell Syst. https:\/\/doi.org\/10.1007\/s40747-024-01458-4","journal-title":"Complex Intell Syst"},{"key":"1676_CR20","doi-asserted-by":"publisher","first-page":"1347","DOI":"10.1007\/s40747-022-00859-7","volume":"9","author":"Y Dong","year":"2023","unstructured":"Dong Y, Jiang Z, Tao F et al (2023) Multiple spatial residual network for object detection. Complex Intell Syst 9:1347\u20131362. https:\/\/doi.org\/10.1007\/s40747-022-00859-7","journal-title":"Complex Intell Syst"},{"key":"1676_CR21","doi-asserted-by":"publisher","first-page":"1680","DOI":"10.3390\/make5040083","volume":"5","author":"JR Terven","year":"2023","unstructured":"Terven JR, Esparza DM, Romero-Gonz\u00e1lez J (2023) A comprehensive review of YOLO architectures in computer vision: from YOLOv1 to YOLOv8 and YOLO-NAS. Mach Learn Knowl Extr 5:1680\u20131716","journal-title":"Mach Learn Knowl Extr"},{"key":"1676_CR22","doi-asserted-by":"publisher","first-page":"770","DOI":"10.1109\/CVPR.2016.90","volume-title":"Deep residual learning for image recognition","author":"K He","year":"2016","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, pp 770\u2013778. https:\/\/doi.org\/10.1109\/CVPR.2016.90"},{"key":"1676_CR23","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46448-0_2","volume-title":"Computer vision ECCV 2016 ECCV 2016. Lecture notes in computer science","author":"W Liu","year":"2016","unstructured":"Liu W et al (2016) SSD: single shot multibox detector. In: Leibe B, Matas J, Sebe N, Welling M (eds) Computer vision ECCV 2016 ECCV 2016. Lecture notes in computer science, vol 9905. Springer, Cham. https:\/\/doi.org\/10.1007\/978-3-319-46448-0_2"},{"key":"1676_CR24","unstructured":"Reis D, Kupec J, Hong J, Daoudi A (2023) Real-time flying object detection with YOLOv8.\u00a0https:\/\/arxiv.org\/abs\/2305.09972"},{"key":"1676_CR25","unstructured":"Hinton GE, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network.\u00a0https:\/\/arxiv.org\/abs\/1503.02531"},{"key":"1676_CR26","doi-asserted-by":"publisher","first-page":"106533","DOI":"10.1016\/j.engappai.2023.106533","volume":"124","author":"C Jin Li","year":"2023","unstructured":"Jin Li C, Qu Z, Ye Wang S (2023) A method of knowledge distillation based on feature fusion and attention mechanism for complex traffic scenes. Eng. Appl. Artif. Intell. 124:106533. https:\/\/doi.org\/10.1016\/j.engappai.2023.106533","journal-title":"Eng. Appl. Artif. Intell."},{"key":"1676_CR27","doi-asserted-by":"publisher","first-page":"1789","DOI":"10.1007\/s11263-021-01453-z","volume":"129","author":"J Gou","year":"2021","unstructured":"Gou J, Yu B, Maybank SJ et al (2021) Knowledge distillation: a survey. Int J Comput Vis 129:1789\u20131819. https:\/\/doi.org\/10.1007\/s11263-021-01453-z","journal-title":"Int J Comput Vis"},{"key":"1676_CR28","unstructured":"Gao M, Shen Y, Li Q, Yan J, Wan L, Lin D, Loy CC, Tang X (2018) An embarrassingly simple approach for knowledge distillation. Comput Vis Pattern Recognit. https:\/\/arxiv.org\/abs\/1708.06519"},{"key":"1676_CR29","first-page":"7341","volume":"2017","author":"Q Li","year":"2017","unstructured":"Li Q, Jin S, Yan J (2017) Mimicking very efficient network for object detection. IEEE Conf Comput Vision Pattern Recogn (CVPR) 2017:7341\u20137349","journal-title":"IEEE Conf Comput Vision Pattern Recogn (CVPR)"},{"key":"1676_CR30","first-page":"4928","volume":"2019","author":"T Wang","year":"2019","unstructured":"Wang T, Yuan L, Zhang X, Feng J (2019) Distilling object detectors with fine-grained feature imitation. IEEE\/CVF Conf Comput Vision Pattern Recogn (CVPR) 2019:4928\u20134937","journal-title":"IEEE\/CVF Conf Comput Vision Pattern Recogn (CVPR)"},{"key":"1676_CR31","doi-asserted-by":"publisher","unstructured":"Heo B, Kim J, Yun S, Park H, Kwak N, Choi JY (2019) A comprehensive overhaul of feature distillation. 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Korea (South). pp. 1921\u20131930. https:\/\/doi.org\/10.1109\/ICCV.2019.00201.","DOI":"10.1109\/ICCV.2019.00201"},{"key":"1676_CR32","first-page":"7130","volume":"2017","author":"J Yim","year":"2017","unstructured":"Yim J, Joo D, Bae J, Kim J (2017) A gift from knowledge distillation: fast optimization, network minimization and transfer learning. IEEE Conf Comput Vision Pattern Recogn (CVPR) 2017:7130\u20137138","journal-title":"IEEE Conf Comput Vision Pattern Recogn (CVPR)"},{"key":"1676_CR33","doi-asserted-by":"publisher","unstructured":"Tung F, Mori G (2019) Similarity-preserving knowledge distillation. 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Korea (South). pp. 1365\u20131374. https:\/\/doi.org\/10.1109\/ICCV.2019.00145.","DOI":"10.1109\/ICCV.2019.00145"},{"key":"1676_CR34","doi-asserted-by":"publisher","unstructured":"Chen P, Liu S, Zhao S, Jia J (2021) Distilling Knowledge via Knowledge Review. 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville. 2021. pp. 5006\u20135015. https:\/\/doi.org\/10.1109\/CVPR46437.2021.00497.","DOI":"10.1109\/CVPR46437.2021.00497"},{"key":"1676_CR35","doi-asserted-by":"publisher","first-page":"79491","DOI":"10.1109\/ACCESS.2022.3194898","volume":"10","author":"S Cho","year":"2022","unstructured":"Cho S, Kim S, Jung S, Ko S (2022) Blur-robust object detection using feature-level deblurring via self-guided knowledge distillation. IEEE Access 10:79491\u201379501","journal-title":"IEEE Access"},{"key":"1676_CR36","doi-asserted-by":"publisher","unstructured":"Tao X, Gao H, Shen X, Wang J, Jia J (2018) Scale-recurrent network for deep image deblurring. 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City. pp. 8174\u20138182. https:\/\/doi.org\/10.1109\/CVPR.2018.00853.","DOI":"10.1109\/CVPR.2018.00853"},{"key":"1676_CR37","doi-asserted-by":"publisher","first-page":"106997","DOI":"10.1016\/j.engappai.2023.106997","volume":"126","author":"Y He","year":"2023","unstructured":"He Y, Li J (2023) TSRes-YOLO: an accurate and fast cascaded detector for waste collection and transportation supervision. Eng. Appl. Artif. Intell. 126:106997. https:\/\/doi.org\/10.1016\/j.engappai.2023.106997","journal-title":"Eng. Appl. Artif. Intell."},{"key":"1676_CR38","doi-asserted-by":"publisher","unstructured":"Woo S, Park J, Lee JY, Kweon IS (2018) CBAM: Convolutional block attention module. In: Ferrari V, Hebert M, Sminchisescu C, Weiss Y (eds) Computer Vision \u2013 ECCV 2018. ECCV 2018. Lecture Notes in Computer Science, vol 11211. Springer, Cham. https:\/\/doi.org\/10.1007\/978-3-030-01234-2_1","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"1676_CR39","unstructured":"Narayanan M (2023). SENetV2: aggregated dense layer for channelwise and global representations. ArXiv, abs\/2311.10807."},{"key":"1676_CR40","doi-asserted-by":"crossref","unstructured":"Xiang Y, Zhou H, Li C, Sun F, Li Z, Xie Y (2024) Application of deep learning in blind motion deblurring: current status and future prospects. http:\/\/arxiv.org\/abs\/2401.05055","DOI":"10.1007\/s00371-024-03632-8"},{"key":"1676_CR41","doi-asserted-by":"publisher","unstructured":"Dai J, et al. (2017) Deformable Convolutional Networks. 2017 IEEE International Conference on Computer Vision (ICCV), Venice. pp. 764\u2013773. https:\/\/doi.org\/10.1109\/ICCV.2017.89.","DOI":"10.1109\/ICCV.2017.89"},{"key":"1676_CR42","unstructured":"Paszke A, Gross S, Massa F, Lerer A, Bradbury J, Chanan G, Killeen T, Lin Z, Gimelshein N, Antiga L, Desmaison A, K\u00f6pf A, Yang E, DeVito Z, Raison M, Tejani A, Chilamkurthy S, Steiner B, Fang L, Bai J, Chintala S (2019) PyTorch: an imperative style, high-performance deep learning library.\u00a0https:\/\/arxiv.org\/abs\/1912.01703"},{"key":"1676_CR43","unstructured":"Tzutalin, \u201clabelImg. 2015. Available online: https:\/\/github.com\/tzutalin\/labelImg (accessed on 27 July 2015)\u201d"},{"key":"1676_CR44","unstructured":"Zhu PF, Wen L, Bian X, Ling H, Hu Q (2018) Vision meets drones: a challenge.\u00a0https:\/\/arxiv.org\/abs\/1804.07437"},{"key":"1676_CR45","doi-asserted-by":"publisher","DOI":"10.1016\/j.spl.2021.109349","author":"X Cheng","year":"2022","unstructured":"Cheng X, Li Y (2022) An improved Hoeffding\u2019s inequality for sum of independent random variables. Stat Prob Lett. https:\/\/doi.org\/10.1016\/j.spl.2021.109349","journal-title":"Stat Prob Lett"},{"key":"1676_CR46","doi-asserted-by":"publisher","unstructured":"Lin TY, Goyal P, Girshick R, He K, Doll\u00e1r P (2017) Focal loss for dense object detection. 2017 IEEE International Conference on Computer Vision (ICCV), Venice. pp. 2999\u20133007. https:\/\/doi.org\/10.1109\/ICCV.2017.324.","DOI":"10.1109\/ICCV.2017.324"},{"key":"1676_CR47","unstructured":"Ultralytics, \u201cYOLOv5. 2020. Available online: https:\/\/github.com\/ultralytics\/yolov5 (accessed on 25 June 2020)\u201d."},{"key":"1676_CR48","unstructured":"Li C, Li L, Jiang H, Weng K, Geng Y, Li L, Ke Z, Li Q, Cheng M, Nie W, Li Y, Zhang B, Liang Y, Zhou L, Xu X, Chu X, Wei X, Wei X (2022) YOLOv6: a single-stage object detection framework for industrial applications.\u00a0ArXiv, abs\/2209.02976."},{"key":"1676_CR49","first-page":"7464","volume":"2023","author":"C Wang","year":"2022","unstructured":"Wang C, Bochkovskiy A, Liao HM (2022) YOLOv7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. IEEE\/CVF Conf Comput Vision Pattern Recogn (CVPR) 2023:7464\u20137475","journal-title":"IEEE\/CVF Conf Comput Vision Pattern Recogn (CVPR)"},{"key":"1676_CR50","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2023.3258666","volume":"61","author":"J Zhang","year":"2023","unstructured":"Zhang J, Lei J, Xie W, Fang Z, Li Y, Du Q (2023) SuperYOLO: super resolution assisted object detection in multimodal remote sensing imagery. IEEE Trans Geosci Remote Sens 61:1\u201315. https:\/\/doi.org\/10.1109\/TGRS.2023.3258666","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"2","key":"1676_CR51","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","volume":"88","author":"M Everingham","year":"2010","unstructured":"Everingham M, Van Gool L, Williams CK, Winn J, Zisserman A (2010) The pascal visual object classes (voc) challenge. Int J Comput Vision 88(2):303\u2013338","journal-title":"Int J Comput Vision"},{"key":"1676_CR52","first-page":"740","volume-title":"Microsoft coco: common objects in context. In: European Conference on Computer Vision","author":"TY Lin","year":"2014","unstructured":"Lin TY, Maire M, Belongie S, Hays J, Perona P, Ramanan D, Dollar P, Zitnick CL (2014) Microsoft coco: common objects in context. In: European Conference on Computer Vision. Springer, Cham, pp 740\u2013755"},{"key":"1676_CR53","doi-asserted-by":"crossref","unstructured":"Geiger A, Lenz P, Urtasun R (2012) Are we ready for autonomous driving? The kitti vision benchmark suite. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition. IEEE. pp. 3354\u20133361.","DOI":"10.1109\/CVPR.2012.6248074"},{"key":"1676_CR54","doi-asserted-by":"crossref","unstructured":"Du D, Qi Y, Yu H, Yang Y, Duan K, Li G, Zhang W, Huang Q, Tian Q (2018) The unmanned aerial vehicle benchmark: Object detection and tracking. Proceedings of the European Conference on Computer Vision. pp. 370\u2013386.","DOI":"10.1007\/978-3-030-01249-6_23"},{"key":"1676_CR55","doi-asserted-by":"publisher","first-page":"147","DOI":"10.3389\/fncom.2014.00147","volume":"8","author":"D Khosla","year":"2014","unstructured":"Khosla D, Chen Y, Kim K (2014) A neuromorphic system for video object recognition. Front Comput Neurosci 8:147","journal-title":"Front Comput Neurosci"},{"key":"1676_CR56","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1016\/j.isprsjprs.2014.10.002","volume":"98","author":"G Cheng","year":"2014","unstructured":"Cheng G, Han J, Zhou P, Guo L (2014) Multi-class geospatial object detection and geographic image classification based on collection of part detectors. ISPRS J Photogramm Remote Sens 98:119\u2013132","journal-title":"ISPRS J Photogramm Remote Sens"},{"key":"1676_CR57","doi-asserted-by":"crossref","unstructured":"Yang Y, Newsam S (2011) Spatial pyramid co-occurrence for image classification. In: 2011 International Conference on Computer Vision. IEEE. pp. 1465\u20131472.","DOI":"10.1109\/ICCV.2011.6126403"},{"key":"1676_CR58","doi-asserted-by":"crossref","unstructured":"Xia G-S, Bai X, Ding J, Zhu Z, Belongie S, Luo J, Datcu M, Pelillo M, Zhang L (2018) Dota: a large-scale dataset for object detection in aerial images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 3974\u20133983.","DOI":"10.1109\/CVPR.2018.00418"},{"key":"1676_CR59","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1016\/j.jvcir.2015.11.002","volume":"34","author":"S Razakarivony","year":"2016","unstructured":"Razakarivony S, Jurie F (2016) Vehicle detection in aerial imagery: a small target detection benchmark. J Vis Commun Image Represent 34:187\u2013203","journal-title":"J Vis Commun Image Represent"},{"key":"1676_CR60","doi-asserted-by":"publisher","first-page":"296","DOI":"10.1016\/j.isprsjprs.2019.11.023","volume":"159","author":"K Li","year":"2020","unstructured":"Li K, Wan G, Cheng G, Meng L, Han J (2020) Object detection in optical remote sensing images: a survey and a new benchmark. ISPRS J Photogramm Remote Sens 159:296\u2013307","journal-title":"ISPRS J Photogramm Remote Sens"},{"key":"1676_CR61","first-page":"1","volume":"72","author":"T Ye","year":"2023","unstructured":"Ye T, Qin W, Zhao Z, Gao X, Deng X, Ouyang Y (2023) Real-time object detection network in uav-vision based on cnn and transformer. IEEE Trans Instrum Meas 72:1\u201313","journal-title":"IEEE Trans Instrum Meas"},{"key":"1676_CR62","unstructured":"Rekavandi AM, Xu L, Boussaid F, Seghouane A, Hoefs S, Bennamoun (2022) A guide to image and video based small object detection using deep learning : case study of maritime surveillance.\u00a0ArXiv, abs\/2207.12926."},{"key":"1676_CR63","unstructured":"Rekavandi AM, Rashidi S, Boussaid F, Hoefs S, Akbas E, Bennamoun\nM (2023) Transformers in small object detection: a benchmark and survey of state-of-the-art.\u00a0https:\/\/arxiv.org\/abs\/2309.04902"},{"key":"1676_CR64","doi-asserted-by":"publisher","unstructured":"Pathak D, Kr\u00e4henb\u00fchl P, Donahue J, Darrell T, Efros AA (2016) Context encoders:feature learning by inpainting. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 2016, pp. 2536\u20132544. https:\/\/doi.org\/10.1109\/CVPR.2016.278","DOI":"10.1109\/CVPR.2016.278"},{"key":"1676_CR65","doi-asserted-by":"crossref","unstructured":"Zhang R, Isola P, Efros AA (2016) Colorful image colorization. In: Leibe B, Matas J, Sebe N, Welling M (eds) ECCV 2016. LNCS vol 9907. Springer, Cham. pp 649\u2013666","DOI":"10.1007\/978-3-319-46487-9_40"},{"key":"1676_CR66","first-page":"9359","volume":"2018","author":"M Noroozi","year":"2018","unstructured":"Noroozi M, Vinjimoor A, Favaro P, Pirsiavash H (2018) Boosting self-supervised learning via knowledge transfer. IEEE\/CVF Conf Comput Vision Pattern Recogn 2018:9359\u20139367","journal-title":"IEEE\/CVF Conf Comput Vision Pattern Recogn"},{"key":"1676_CR67","volume-title":"Knowledge distillation meets self-supervision. European Conference on Computer Vision","author":"G Xu","year":"2020","unstructured":"Xu G, Liu Z, Li X, Loy CC (2020) Knowledge distillation meets self-supervision. European Conference on Computer Vision. Springer International Publishing, Cham"},{"key":"1676_CR68","doi-asserted-by":"crossref","unstructured":"Yin J, Qiu J, Zhang S, Ma Z, Guo J (2020) SSKD: Self-Supervised Knowledge Distillation for Cross Domain Adaptive Person Re-Identification. 2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC). pp 81\u201385.","DOI":"10.1109\/IC-NIDC54101.2021.9660538"},{"key":"1676_CR69","first-page":"6687","volume":"2020","author":"X Zhan","year":"2020","unstructured":"Zhan X, Xie J, Liu Z, Ong YS, Loy CC (2020) Online deep clustering for unsupervised representation learning. IEEE\/CVF Conf Comput Vision Pattern Recogn (CVPR) 2020:6687\u20136696","journal-title":"IEEE\/CVF Conf Comput Vision Pattern Recogn (CVPR)"},{"key":"1676_CR70","unstructured":"Donahue J, Simonyan K (2019) Large scale adversarial representation learning. In:Advances in Neural Information Processing Systems. pp. 10541\u201310551"},{"key":"1676_CR71","unstructured":"Dumoulin V, Belghazi I, Poole B, Lamb A, Arjovsky M, Mastropietro O, Courville AC (2016) Adversarially learned inference. https:\/\/arxiv.org\/abs\/1606.00704"},{"key":"1676_CR72","unstructured":"Chen T, Kornblith S, Norouzi M, Hinton GE (2020) A simple framework for contrastive learning of visual representations. https:\/\/arxiv.org\/abs\/2002.05709"},{"key":"1676_CR73","unstructured":"H\u00e9naff OJ, Srinivas A, Fauw JD, Razavi A, Doersch C, Eslami SM, Oord AV (2019) Data-efficient image recognition with contrastive predictive coding. https:\/\/arxiv.org\/abs\/1905.09272"},{"key":"1676_CR74","unstructured":"Balestriero R, Ibrahim M, Sobal V, Morcos AS, Shekhar S, Goldstein T, Bordes F, Bardes A, Mialon G, Tian Y, Schwarzschild A, Wilson AG, Geiping J, Garrido Q, Fernandez P, Bar A, Pirsiavash H, LeCun Y, Goldblum M (2023) A cookbook of self-supervised learning. https:\/\/arxiv.org\/abs\/2304.12210"},{"issue":"12","key":"1676_CR75","doi-asserted-by":"publisher","first-page":"9052","DOI":"10.1109\/TPAMI.2024.3415112","volume":"46","author":"J Gui","year":"2023","unstructured":"Gui J, Chen T, Zhang J, Cao Q, Sun Z, Luo H, Tao D (2023) A survey on self-supervised learning: algorithms, applications, and future trends.  IEEE Trans Pattern Anal Mach Intell 46(12):9052\u20139071. https:\/\/doi.org\/10.1109\/TPAMI.2024.3415112","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"9","key":"1676_CR76","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 (2015) Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans Pattern Anal Mach Intell. 37(9):1904\u20131916. https:\/\/doi.org\/10.1109\/TPAMI.2015.2389824","journal-title":"IEEE Trans Pattern Anal Mach Intell."},{"key":"1676_CR77","unstructured":"Glorot Xavier, Yoshua Bengio (2010) Understanding the difficulty of training deep feedforward neural networks. International Conference on Artificial Intelligence and Statistics."}],"container-title":["Complex & Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-024-01676-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s40747-024-01676-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-024-01676-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,30]],"date-time":"2025-01-30T20:22:02Z","timestamp":1738268522000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s40747-024-01676-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,4]]},"references-count":77,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["1676"],"URL":"https:\/\/doi.org\/10.1007\/s40747-024-01676-w","relation":{},"ISSN":["2199-4536","2198-6053"],"issn-type":[{"value":"2199-4536","type":"print"},{"value":"2198-6053","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,4]]},"assertion":[{"value":"13 May 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 November 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 December 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 known competing financial interests or personal relationships that could have influenced the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"63"}}