{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T16:26:38Z","timestamp":1788539198550,"version":"build-2803163510"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2023,9,23]],"date-time":"2023-09-23T00:00:00Z","timestamp":1695427200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,9,23]],"date-time":"2023-09-23T00:00:00Z","timestamp":1695427200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"the National Key Research and Development Program of China","award":["2018YFB1306600"],"award-info":[{"award-number":["2018YFB1306600"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62076207"],"award-info":[{"award-number":["62076207"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62076208"],"award-info":[{"award-number":["62076208"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U20A20227"],"award-info":[{"award-number":["U20A20227"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. & Cyber."],"published-print":{"date-parts":[[2024,3]]},"DOI":"10.1007\/s13042-023-01943-1","type":"journal-article","created":{"date-parts":[[2023,9,23]],"date-time":"2023-09-23T13:01:51Z","timestamp":1695474111000},"page":"853-862","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Feature augmentation and scale penalty for tiny floating detection"],"prefix":"10.1007","volume":"15","author":[{"given":"Ke","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yining","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wang","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Siyuan","family":"Shen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shukai","family":"Duan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lidan","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,9,23]]},"reference":[{"key":"1943_CR1","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1016\/j.neucom.2020.10.081","volume":"429","author":"M Wang","year":"2021","unstructured":"Wang M, Deng W (2021) Deep face recognition: a survey. Neurocomputing 429:215\u2013244","journal-title":"Neurocomputing"},{"key":"1943_CR2","doi-asserted-by":"crossref","unstructured":"Sundararaman R, De\u00a0Almeida\u00a0Braga C, Marchand E, Pettre J (2021) Tracking pedestrian heads in dense crowd. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 3865\u20133875","DOI":"10.1109\/CVPR46437.2021.00386"},{"key":"1943_CR3","doi-asserted-by":"crossref","unstructured":"Prakash A, Chitta K, Geiger A (2021) Multi-modal fusion transformer for end-to-end autonomous driving. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 7077\u20137087","DOI":"10.1109\/CVPR46437.2021.00700"},{"key":"1943_CR4","doi-asserted-by":"crossref","unstructured":"Han J, Ding J, Xue N, Xia G-S (2021) Redet: a rotation-equivariant detector for aerial object detection. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 2786\u20132795","DOI":"10.1109\/CVPR46437.2021.00281"},{"key":"1943_CR5","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1016\/j.neucom.2021.12.008","volume":"473","author":"D Medak","year":"2022","unstructured":"Medak D, Posilovi\u0107 L, Suba\u0161i\u0107 M, Budimir M, Lon\u010dari\u0107 S (2022) DefectDet: a deep learning architecture for detection of defects with extreme aspect ratios in ultrasonic images. Neurocomputing 473:107\u2013115","journal-title":"Neurocomputing"},{"key":"1943_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2021.107846","volume":"112","author":"K Wang","year":"2021","unstructured":"Wang K, Liu M, Ye Z (2021) An advanced YOLOv3 method for small-scale road object detection. Appl Soft Comput 112:107846","journal-title":"Appl Soft Comput"},{"key":"1943_CR7","unstructured":"Bochkovskiy A, Wang C-Y, Liao H-YM (2020) YOLOv4: optimal speed and accuracy of object detection, arXiv preprintarXiv:2004.10934"},{"key":"1943_CR8","doi-asserted-by":"publisher","first-page":"13895","DOI":"10.1007\/s00521-021-06029-z","volume":"35","author":"R Gai","year":"2023","unstructured":"Gai R, Chen N, Yuan H (2023) A detection algorithm for cherry fruits based on the improved YOLO-v4 model. Neural Comput Appl 35:13895\u201313906","journal-title":"Neural Comput Appl"},{"key":"1943_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2021.106135","volume":"185","author":"X Hu","year":"2021","unstructured":"Hu X, Liu Y, Zhao Z, Liu J, Yang X, Sun C, Chen S, Li B, Zhou C (2021) Real-time detection of uneaten feed pellets in underwater images for aquaculture using an improved YOLO-v4 network. Comput Electron Agric 185:106135","journal-title":"Comput Electron Agric"},{"key":"1943_CR10","doi-asserted-by":"crossref","unstructured":"Tzou T-L, Huang C-H, Lai Y-H, Tsai M-H, Hsu C-T, Chen P-S, Lee W-J (2022) Detect safety net on the construction site based on YOLO-v4. In: Innovative computing. Springer, pp 33\u201342","DOI":"10.1007\/978-981-16-4258-6_5"},{"issue":"1","key":"1943_CR11","first-page":"526","volume":"60","author":"Z-H Chen","year":"2022","unstructured":"Chen Z-H, Juang J-C (2022) YOLOv4 object detection model for nondestructive radiographic testing in aviation maintenance tasks. AIAA J 60(1):526\u2013531","journal-title":"AIAA J"},{"key":"1943_CR12","doi-asserted-by":"crossref","unstructured":"Woo S, Park J, Lee J-Y, Kweon IS (2018) CBAM: convolutional block attention module. In: Proceedings of the European conference on computer vision (ECCV), pp 3\u201319","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"1943_CR13","doi-asserted-by":"crossref","unstructured":"Liu S, Qi L, Qin H, Shi J, Jia J (2018) Path aggregation network for instance segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 8759\u20138768","DOI":"10.1109\/CVPR.2018.00913"},{"issue":"12","key":"1943_CR14","doi-asserted-by":"publisher","first-page":"17263","DOI":"10.1007\/s11042-022-12686-6","volume":"81","author":"Z Yi","year":"2022","unstructured":"Yi Z, Yao D, Li G, Ai J, Xie W (2022) Detection and localization for lake floating objects based on CA-Faster R-CNN. Multimed Tools Appl 81(12):17263\u201317281","journal-title":"Multimed Tools Appl"},{"issue":"18","key":"1943_CR15","doi-asserted-by":"publisher","first-page":"11729","DOI":"10.3390\/su141811729","volume":"14","author":"N Li","year":"2022","unstructured":"Li N, Huang H, Wang X, Yuan B, Liu Y, Xu S (2022) Detection of floating garbage on water surface based on PC-Net. Sustainability 14(18):11729","journal-title":"Sustainability"},{"issue":"9","key":"1943_CR16","doi-asserted-by":"publisher","first-page":"1111","DOI":"10.3390\/e23091111","volume":"23","author":"F Lin","year":"2021","unstructured":"Lin F, Hou T, Jin Q, You A (2021) Improved YOLO based detection algorithm for floating debris in waterway. Entropy 23(9):1111","journal-title":"Entropy"},{"key":"1943_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.120185","volume":"225","author":"C Renfei","year":"2023","unstructured":"Renfei C, Jian W, Yong P, Zhongwen L, Hua S (2023) Detection and tracking of floating objects based on spatial\u2013temporal information fusion. Expert Syst Appl 225:120185","journal-title":"Expert Syst Appl"},{"key":"1943_CR18","doi-asserted-by":"publisher","first-page":"81 147","DOI":"10.1109\/ACCESS.2021.3085348","volume":"9","author":"L Zhang","year":"2021","unstructured":"Zhang L, Wei Y, Wang H, Shao Y, Shen J (2021) Real-time detection of river surface floating object based on improved RefineDet. IEEE Access 9:81 147-81 160","journal-title":"IEEE Access"},{"key":"1943_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.dsp.2022.103442","volume":"123","author":"X Yu","year":"2022","unstructured":"Yu X, Ye X, Zhang S (2022) Floating pollutant image target extraction algorithm based on immune extremum region. Digital Signal Process 123:103442","journal-title":"Digital Signal Process"},{"key":"1943_CR20","doi-asserted-by":"crossref","unstructured":"Cheng Y, Xu H, Liu Y (2021) Robust small object detection on the water surface through fusion of camera and millimeter wave radar. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 15263\u201315272","DOI":"10.1109\/ICCV48922.2021.01498"},{"key":"1943_CR21","doi-asserted-by":"crossref","unstructured":"Lin T-Y, Maire M, Belongie S, Hays J, Perona P, Ramanan D, Doll\u00e1r P, Zitnick CL (2014) Microsoft coco: common objects in context. In: European conference on computer vision. Springer, pp 740\u2013755","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"1943_CR22","doi-asserted-by":"crossref","unstructured":"Chen C, Liu M-Y, Tuzel O, Xiao J (2016) R-CNN for small object detection. In: Asian conference on computer vision. Springer, pp 214\u2013230","DOI":"10.1007\/978-3-319-54193-8_14"},{"key":"1943_CR23","doi-asserted-by":"crossref","unstructured":"Kisantal M, Wojna Z, Murawski J, Naruniec J, Cho K (2019) Augmentation for small object detection, arXiv preprintarXiv:1902.07296","DOI":"10.5121\/csit.2019.91713"},{"key":"1943_CR24","doi-asserted-by":"crossref","unstructured":"Yu X, Gong Y, Jiang N, Ye Q, Han Z (2020) Scale match for tiny person detection. In: Proceedings of the IEEE\/CVF winter conference on applications of computer vision, pp 1257\u20131265","DOI":"10.1109\/WACV45572.2020.9093394"},{"key":"1943_CR25","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: European conference on computer vision. Springer, pp 21\u201337","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"1943_CR26","doi-asserted-by":"crossref","unstructured":"Lin T-Y, Doll\u00e1r P, Girshick R, He K, Hariharan B, Belongie S (2017) Feature pyramid networks for object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2117\u20132125","DOI":"10.1109\/CVPR.2017.106"},{"key":"1943_CR27","doi-asserted-by":"publisher","first-page":"287","DOI":"10.1016\/j.neucom.2020.12.093","volume":"433","author":"J Leng","year":"2021","unstructured":"Leng J, Ren Y, Jiang W, Sun X, Wang Y (2021) Realize your surroundings: exploiting context information for small object detection. Neurocomputing 433:287\u2013299","journal-title":"Neurocomputing"},{"key":"1943_CR28","doi-asserted-by":"crossref","unstructured":"Bai Y, Zhang Y, Ding M, Ghanem B (2018) SOD-MTGAN: small object detection via multi-task generative adversarial network. In: Proceedings of the European conference on computer vision (ECCV), pp 206\u2013221","DOI":"10.1007\/978-3-030-01261-8_13"},{"key":"1943_CR29","doi-asserted-by":"crossref","unstructured":"Cai Z, Vasconcelos N (2018) Cascade R-CNN: delving into high quality object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 6154\u20136162","DOI":"10.1109\/CVPR.2018.00644"},{"key":"1943_CR30","unstructured":"Yu F, Koltun V (2015) Multi-scale context aggregation by dilated convolutions, arXiv preprintarXiv:1511.07122"},{"key":"1943_CR31","unstructured":"Jocher G (2022) ultralytics\/yolov5: v6.1 [Online]. https:\/\/github.com\/ultralytics\/yolov5"},{"key":"1943_CR32","unstructured":"Cheng G, Yuan X, Yao X, Yan K, Zeng Q, Han J (2022) Towards large-scale small object detection: survey and benchmarks, arXiv preprintarXiv:2207.14096"},{"key":"1943_CR33","doi-asserted-by":"crossref","unstructured":"Lim J-S, Astrid M, Yoon H-J, Lee S-I (2021) Small object detection using context and attention. In: 2021 International conference on artificial intelligence in information and communication (ICAIIC). IEEE, pp 181\u2013186","DOI":"10.1109\/ICAIIC51459.2021.9415217"},{"issue":"4","key":"1943_CR34","doi-asserted-by":"publisher","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","volume":"40","author":"L-C Chen","year":"2017","unstructured":"Chen L-C, Papandreou G, Kokkinos I, Murphy K, Yuille AL (2017) Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans Pattern Anal Mach Intell 40(4):834\u2013848","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1943_CR35","doi-asserted-by":"publisher","first-page":"108438","DOI":"10.1109\/ACCESS.2020.3001234","volume":"8","author":"D Sun","year":"2020","unstructured":"Sun D, Yang Y, Li M, Yang J, Meng B, Bai R, Li L, Ren J (2020) A scale balanced loss for bounding box regression. IEEE Access 8:108438\u2013108448","journal-title":"IEEE Access"},{"key":"1943_CR36","doi-asserted-by":"crossref","unstructured":"Cheng Y, Zhu J, Jiang M, Fu J, Pang C, Wang P, Sankaran K, Onabola O, Liu Y, Liu D et al (2021) Flow: a dataset and benchmark for floating waste detection in inland waters. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 10953\u201310962","DOI":"10.1109\/ICCV48922.2021.01077"},{"key":"1943_CR37","unstructured":"Loshchilov I, Hutter F (2016) SGDR: stochastic gradient descent with warm restarts. arXiv preprintarXiv:1608.03983"},{"key":"1943_CR38","unstructured":"Kingma DP, Ba J (2014) Adam: a method for stochastic optimization, arXiv preprintarXiv:1412.6980"},{"key":"1943_CR39","doi-asserted-by":"crossref","unstructured":"Lin T-Y, Goyal P, Girshick R, He K, Doll\u00e1r P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980\u20132988","DOI":"10.1109\/ICCV.2017.324"},{"issue":"10.5555","key":"1943_CR40","first-page":"2 969 239","volume":"9199","author":"R Faster","year":"2015","unstructured":"Faster R (2015) Towards real-time object detection with region proposal networks. Adv Neural Inf Process Syst 9199(10.5555):2 969 239-2 969 250","journal-title":"Adv Neural Inf Process Syst"},{"key":"1943_CR41","unstructured":"Ge Z, Liu S, Wang F, Li Z, Sun J (2021) YOLOX: exceeding yolo series in 2021, arXiv preprintarXiv:2107.08430"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-023-01943-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-023-01943-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-023-01943-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,10]],"date-time":"2024-02-10T10:29:50Z","timestamp":1707560990000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-023-01943-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,23]]},"references-count":41,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2024,3]]}},"alternative-id":["1943"],"URL":"https:\/\/doi.org\/10.1007\/s13042-023-01943-1","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,23]]},"assertion":[{"value":"13 July 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 July 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 September 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}