{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T19:37:33Z","timestamp":1757619453413,"version":"3.44.0"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2025,7,23]],"date-time":"2025-07-23T00:00:00Z","timestamp":1753228800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,7,23]],"date-time":"2025-07-23T00:00:00Z","timestamp":1753228800000},"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":["Pattern Anal Applic"],"published-print":{"date-parts":[[2025,9]]},"DOI":"10.1007\/s10044-025-01528-4","type":"journal-article","created":{"date-parts":[[2025,7,23]],"date-time":"2025-07-23T11:44:10Z","timestamp":1753271050000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Lyolo: a lightweight object detection algorithm integrating label enhancement for high-quality prediction boxes"],"prefix":"10.1007","volume":"28","author":[{"given":"Ruxin","family":"Gao","sequence":"first","affiliation":[]},{"given":"Zhiyong","family":"Ling","sequence":"additional","affiliation":[]},{"given":"Chengyang","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Xiang","family":"Li","sequence":"additional","affiliation":[]},{"given":"Jianmin","family":"She","sequence":"additional","affiliation":[]},{"given":"Qunpo","family":"Liu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,7,23]]},"reference":[{"doi-asserted-by":"crossref","unstructured":"Redmon J (2016) You only look once: unified, real-time object detection","key":"1528_CR1","DOI":"10.1109\/CVPR.2016.91"},{"doi-asserted-by":"crossref","unstructured":"Redmon J, Farhadi A (2017) Yolo9000: better, faster, stronger, 7263\u20137271","key":"1528_CR2","DOI":"10.1109\/CVPR.2017.690"},{"unstructured":"Redmon J (2018) Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767","key":"1528_CR3"},{"unstructured":"Bochkovskiy A, Wang C-Y, Liao H-YM (2020) Yolov4: Optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934","key":"1528_CR4"},{"unstructured":"Jocher G et\u00a0al. (2022) ultralytics\/yolov5: v7. 0-yolov5 sota realtime instance segmentation. Zenodo","key":"1528_CR5"},{"unstructured":"Li C et\u00a0al. (2023) Yolov6 v3. 0: A full-scale reloading. arXiv preprint arXiv:2301.05586","key":"1528_CR6"},{"unstructured":"Lyu C et\u00a0al. (2022) Rtmdet: an empirical study of designing real-time object detectors. arXiv preprintarXiv:2212.07784","key":"1528_CR7"},{"doi-asserted-by":"crossref","unstructured":"Wang C-Y, Bochkovskiy A, Liao H-YM (2023) Yolov7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors, 7464\u20137475","key":"1528_CR8","DOI":"10.1109\/CVPR52729.2023.00721"},{"unstructured":"Xu S et\u00a0al. (2022) Pp-yoloe: An evolved version of yolo. arXiv preprint arXiv:2203.16250","key":"1528_CR9"},{"unstructured":"Xu X et\u00a0al. (2022) Damo-yolo: a report on real-time object detection design. arXiv preprintarXiv:2211.15444","key":"1528_CR10"},{"doi-asserted-by":"crossref","unstructured":"Wang C-Y, Yeh I-H, Mark\u00a0Liao H-Y (2025) Yolov9: learning what you want to learn using programmable gradient information. Springer, pp 1\u201321","key":"1528_CR11","DOI":"10.1007\/978-3-031-72751-1_1"},{"unstructured":"Wang A et\u00a0al. (2024) Yolov10: real-time end-to-end object detection. arXiv preprintarXiv:2405.14458","key":"1528_CR12"},{"unstructured":"Targ S, Almeida D, Lyman K (2016) Resnet in resnet: generalizing residual architectures. arXiv preprint  arXiv:1603.08029","key":"1528_CR13"},{"doi-asserted-by":"crossref","unstructured":"Girshick, R (2015) Fast r-cnn. arXiv preprint arXiv:1504.08083","key":"1528_CR14","DOI":"10.1109\/ICCV.2015.169"},{"doi-asserted-by":"crossref","unstructured":"Ma N, Zhang X, Zheng H-T, Sun J (2018) Shufflenet v2: practical guidelines for efficient CNN architecture design. pp 116\u2013131","key":"1528_CR15","DOI":"10.1007\/978-3-030-01264-9_8"},{"doi-asserted-by":"crossref","unstructured":"Zhang X, Zhou X, Lin M, Sun J (2018) Shufflenet: an extremely efficient convolutional neural network for mobile devices. pp 6848\u20136856","key":"1528_CR16","DOI":"10.1109\/CVPR.2018.00716"},{"doi-asserted-by":"crossref","unstructured":"Liu W et\u00a0al. (2016) Ssd: single shot multibox detector, 21\u201337 Springer","key":"1528_CR17","DOI":"10.1007\/978-3-319-46448-0_2"},{"doi-asserted-by":"crossref","unstructured":"Tan M, Pang R, Le QV (2020) Efficientdet: scalable and efficient object detection. pp 10781\u201310790","key":"1528_CR18","DOI":"10.1109\/CVPR42600.2020.01079"},{"unstructured":"Ross T-Y, Doll\u00e1r G (2017) Focal loss for dense object detection. pp 2980\u20132988","key":"1528_CR19"},{"key":"1528_CR20","doi-asserted-by":"publisher","first-page":"1407839","DOI":"10.3389\/fpls.2024.1407839","volume":"15","author":"X Wu","year":"2024","unstructured":"Wu X et al (2024) A lightweight grape detection model in natural environments based on an enhanced yolov8 framework. Front Plant Sci 15:1407839","journal-title":"Front Plant Sci"},{"key":"1528_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2024.112204","volume":"300","author":"MJ Karim","year":"2024","unstructured":"Karim MJ, Nahiduzzaman M, Ahsan M, Haider J (2024) Development of an early detection and automatic targeting system for cotton weeds using an improved lightweight yolov8 architecture on an edge device. Knowl-Based Syst 300:112204","journal-title":"Knowl-Based Syst"},{"key":"1528_CR22","first-page":"6002405","volume":"22","author":"Y Zhang","year":"2024","unstructured":"Zhang Y, Zhen J, Liu T, Yang Y, Cheng Y (2024) Adaptive differentiation siamese fusion network for remote sensing change detection. IEEE Geosci Remote Sens Lett 22:6002405","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"1528_CR23","first-page":"1","volume":"61","author":"Y Zhang","year":"2023","unstructured":"Zhang Y, Wu C, Guo W, Zhang T, Li W (2023) Cfanet: Efficient detection of uav image based on cross-layer feature aggregation. IEEE Trans Geosci Remote Sens 61:1\u201311","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"1528_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2024.3510781","volume":"62","author":"Y Zhang","year":"2024","unstructured":"Zhang Y, Wu C, Zhang T, Zheng Y (2024) Full-scale feature aggregation and grouping feature reconstruction based uav image target detection. IEEE Trans Geosci Remote Sens 62:1\u201311","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"1528_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/LGRS.2025.3603339","volume":"22","author":"Y Zhang","year":"2025","unstructured":"Zhang Y, Liu T, Zhen J, Kang Y, Cheng Y (2025) Adaptive downsampling and scale enhanced detection head for tiny object detection in remote sensing image. IEEE Geosci Remote Sens Lett 22:1\u20135","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"1528_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.optlaseng.2024.108170","volume":"178","author":"H Zheng","year":"2024","unstructured":"Zheng H, Chen X, Cheng H, Du Y, Jiang Z (2024) Md-yolo: surface defect detector for industrial complex environments. Opt Lasers Eng 178:108170","journal-title":"Opt Lasers Eng"},{"key":"1528_CR27","doi-asserted-by":"publisher","first-page":"26877","DOI":"10.1109\/JSEN.2024.3418618","volume":"24","author":"W-L Mao","year":"2024","unstructured":"Mao W-L, Wang C-C, Chou P-H, Liu Y-T (2024) Automated defect detection in mass-produced electronic components based on YOLO object detection models. IEEE Sens J 24:26877\u201326888","journal-title":"IEEE Sens J"},{"unstructured":"Zhu X et\u00a0al. (2020) Deformable detr: Deformable transformers for end-to-end object detection. arXiv preprint  arXiv:2010.04159","key":"1528_CR28"},{"doi-asserted-by":"crossref","unstructured":"Zhao Y et\u00a0al. (2024) Detrs beat yolos on real-time object detection, 16965\u201316974","key":"1528_CR29","DOI":"10.1109\/CVPR52733.2024.01605"},{"key":"1528_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2024.109806","volume":"141","author":"Y Zhang","year":"2025","unstructured":"Zhang Y, Zhang T, Wang S, Yu P (2025) An efficient perceptual video compression scheme based on deep learning-assisted video saliency and just noticeable distortion. Eng Appl Artif Intell 141:109806","journal-title":"Eng Appl Artif Intell"},{"key":"1528_CR31","first-page":"013005","volume":"34","author":"Y Zhang","year":"2025","unstructured":"Zhang Y, Wang S, Zhang Y, Yu P (2025) Asymmetric light-aware progressive decoding network for rgb-thermal salient object detection. J Electron Imaging 34:013005\u2013013005","journal-title":"J Electron Imaging"},{"key":"1528_CR32","doi-asserted-by":"publisher","first-page":"2775","DOI":"10.1109\/TCSVT.2023.3312325","volume":"34","author":"Y Zhang","year":"2023","unstructured":"Zhang Y, Liu Y, Kang W, Tao R (2023) Vss-net: visual semantic self-mining network for video summarization. IEEE Trans Circuits Syst Video Technol 34:2775\u20132788","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"1528_CR33","doi-asserted-by":"publisher","first-page":"4183","DOI":"10.1109\/TMM.2023.3321394","volume":"26","author":"Y Zhang","year":"2023","unstructured":"Zhang Y, Zhang T, Wu C, Tao R (2023) Multi-scale spatiotemporal feature fusion network for video saliency prediction. IEEE Trans Multimed 26:4183\u20134193","journal-title":"IEEE Trans Multimed"},{"doi-asserted-by":"crossref","unstructured":"Zhang, Y., Liu, Y (2024) Contextual correspondence matters: bidirectional graph matching for video summarization. Springer, pp 300\u2013317","key":"1528_CR34","DOI":"10.1007\/978-3-031-73021-4_18"},{"key":"1528_CR35","first-page":"1","volume":"73","author":"J Shen","year":"2024","unstructured":"Shen J, Liu N, Sun H, Li D, Zhang Y (2024) An instrument indication acquisition algorithm based on lightweight deep convolutional neural network and hybrid attention fine-grained features. IEEE Trans Instrum Meas 73:1\u201316","journal-title":"IEEE Trans Instrum Meas"},{"key":"1528_CR36","first-page":"1","volume":"71","author":"J Shen","year":"2021","unstructured":"Shen J et al (2021) Finger vein recognition algorithm based on lightweight deep convolutional neural network. IEEE Trans Instrum Meas 71:1\u201313","journal-title":"IEEE Trans Instrum Meas"},{"key":"1528_CR37","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1038\/s40494-025-01565-6","volume":"13","author":"J Shen","year":"2025","unstructured":"Shen J et al (2025) An algorithm based on lightweight semantic features for ancient mural element object detection. NPJ Heritage Science 13:70","journal-title":"NPJ Heritage Science"},{"doi-asserted-by":"crossref","unstructured":"Feng C, Zhong Y, Gao Y, Scott MR, Huang W (2021) Tood: Task-aligned one-stage object detection. IEEE Computer Society, pp 3490\u20133499","key":"1528_CR38","DOI":"10.1109\/ICCV48922.2021.00349"},{"doi-asserted-by":"crossref","unstructured":"Cai X et\u00a0al. (2024) Poly kernel inception network for remote sensing detection, 27706\u201327716","key":"1528_CR39","DOI":"10.1109\/CVPR52733.2024.02617"},{"unstructured":"Tan M, Le Q (2021) Efficientnetv2: smaller models and faster training, 10096\u201310106 (PMLR)","key":"1528_CR40"},{"unstructured":"Howard AG et\u00a0al. (2017) Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861","key":"1528_CR41"},{"doi-asserted-by":"crossref","unstructured":"Sandler M, Howard A, Zhu M, Zhmoginov A, Chen L-C (2018) Mobilenetv2: inverted residuals and linear bottlenecks, 4510\u20134520","key":"1528_CR42","DOI":"10.1109\/CVPR.2018.00474"}],"container-title":["Pattern Analysis and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10044-025-01528-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10044-025-01528-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10044-025-01528-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,7]],"date-time":"2025-09-07T19:42:17Z","timestamp":1757274137000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10044-025-01528-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,23]]},"references-count":42,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2025,9]]}},"alternative-id":["1528"],"URL":"https:\/\/doi.org\/10.1007\/s10044-025-01528-4","relation":{},"ISSN":["1433-7541","1433-755X"],"issn-type":[{"type":"print","value":"1433-7541"},{"type":"electronic","value":"1433-755X"}],"subject":[],"published":{"date-parts":[[2025,7,23]]},"assertion":[{"value":"25 April 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 July 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 July 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":"147"}}