{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T19:17:15Z","timestamp":1783192635506,"version":"3.54.6"},"reference-count":44,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100017291","name":"Zhongshan Science and Technology Bureau","doi-asserted-by":"publisher","award":["2024B2033"],"award-info":[{"award-number":["2024B2033"]}],"id":[{"id":"10.13039\/501100017291","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017291","name":"Zhongshan Science and Technology Bureau","doi-asserted-by":"publisher","award":["SZP202102"],"award-info":[{"award-number":["SZP202102"]}],"id":[{"id":"10.13039\/501100017291","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Image and Vision Computing"],"published-print":{"date-parts":[[2026,8]]},"DOI":"10.1016\/j.imavis.2026.106045","type":"journal-article","created":{"date-parts":[[2026,5,28]],"date-time":"2026-05-28T23:56:57Z","timestamp":1780012617000},"page":"106045","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Protocol-consistent Sigmoid-\u03c4 logit distillation for lightweight object detection"],"prefix":"10.1016","volume":"172","author":[{"given":"Xiangqun","family":"Shi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xun","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xian","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yifan","family":"Su","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"13","key":"10.1016\/j.imavis.2026.106045_b1","doi-asserted-by":"crossref","first-page":"7533","DOI":"10.3390\/app15137533","article-title":"Key considerations for real-time object recognition on edge computing devices","volume":"15","author":"Surantha","year":"2025","journal-title":"Appl. Sci."},{"key":"10.1016\/j.imavis.2026.106045_b2","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2024.108701","article-title":"A lightweight SOD-YOLOv5n model-based winter jujube detection and counting method deployed on android","volume":"218","author":"Yu","year":"2024","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.imavis.2026.106045_b3","series-title":"NIPS Deep Learning and Representation Learning Workshop","article-title":"Distilling the knowledge in a neural network","author":"Hinton","year":"2015"},{"issue":"6","key":"10.1016\/j.imavis.2026.106045_b4","doi-asserted-by":"crossref","first-page":"1789","DOI":"10.1007\/s11263-021-01453-z","article-title":"Knowledge distillation: A survey","volume":"129","author":"Gou","year":"2021","journal-title":"Int. J. Comput. Vis."},{"key":"10.1016\/j.imavis.2026.106045_b5","doi-asserted-by":"crossref","unstructured":"T. Wang, L. Yuan, X.P. Zhang, J.S. Feng, Distilling Object Detectors With Fine-Grained Feature Imitation, in: Proc. IEEE\/CVF Conf. Comput. Vis. Pattern Recognit., CVPR, 2019, pp. 4933\u20134942.","DOI":"10.1109\/CVPR.2019.00507"},{"key":"10.1016\/j.imavis.2026.106045_b6","doi-asserted-by":"crossref","unstructured":"X. Dai, Z. Jiang, Z. Wu, Y.P. Bao, Z.C. Wang, S. Liu, E.J. Zhou, General Instance Distillation for Object Detection, in: Proc. IEEE\/CVF Conf. Comput. Vis. Pattern Recognit., CVPR, 2021, pp. 7842\u20137851.","DOI":"10.1109\/CVPR46437.2021.00775"},{"key":"10.1016\/j.imavis.2026.106045_b7","first-page":"1306","article-title":"Knowledge distillation for object detection via rank mimicking and prediction-guided feature imitation","volume":"vol. 36","author":"Li","year":"2022"},{"issue":"12","key":"10.1016\/j.imavis.2026.106045_b8","doi-asserted-by":"crossref","first-page":"15706","DOI":"10.1109\/TPAMI.2023.3300470","article-title":"Structured knowledge distillation for accurate and efficient object detection","volume":"45","author":"Zhang","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.imavis.2026.106045_b9","doi-asserted-by":"crossref","DOI":"10.1016\/j.imavis.2025.105514","article-title":"MFKD: Multi-dimensional feature alignment for knowledge distillation","volume":"157","author":"Guo","year":"2025","journal-title":"Image Vis. Comput."},{"key":"10.1016\/j.imavis.2026.106045_b10","doi-asserted-by":"crossref","unstructured":"T. Feng, M. Wang, H. Yuan, Overcoming Catastrophic Forgetting in Incremental Object Detection via Elastic Response Distillation, in: Proc. IEEE\/CVF Conf. Comput. Vis. Pattern Recognit., CVPR, 2022, pp. 9427\u20139436.","DOI":"10.1109\/CVPR52688.2022.00921"},{"issue":"3","key":"10.1016\/j.imavis.2026.106045_b11","first-page":"5459","article-title":"ENSOCOM: Ensemble of multi-output neural network\u2019s components for multi-label classification","volume":"72","author":"Alzhrani","year":"2022","journal-title":"Comput. Mater. Contin."},{"key":"10.1016\/j.imavis.2026.106045_b12","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/j.neucom.2020.06.117","article-title":"Deep neural network to extract high-level features and labels in multi-label classification problems","volume":"413","author":"Bello","year":"2020","journal-title":"Neurocomputing"},{"key":"10.1016\/j.imavis.2026.106045_b13","doi-asserted-by":"crossref","unstructured":"L.R. Yang, X.P. Zhou, X.W. Li, L. Qiao, Z.Y. Li, Z.W. Yang, G.A. Wang, X. Li, Bridging Cross-Task Protocol Inconsistency for Distillation in Dense Object Detection, in: Proc. IEEE\/CVF Int. Conf. Comput. Vis., ICCV, 2023, pp. 17175\u201317184.","DOI":"10.1109\/ICCV51070.2023.01575"},{"issue":"12","key":"10.1016\/j.imavis.2026.106045_b14","doi-asserted-by":"crossref","first-page":"24330","DOI":"10.1109\/TITS.2022.3203715","article-title":"An anchor-free lightweight deep convolutional network for vehicle detection in aerial images","volume":"23","author":"Shen","year":"2022","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"10.1016\/j.imavis.2026.106045_b15","first-page":"1","article-title":"An instrument indication acquisition algorithm based on lightweight deep convolutional neural network and hybrid attention fine-grained features","volume":"73","author":"Shen","year":"2024","journal-title":"IEEE Trans. Instrum. Meas."},{"issue":"4","key":"10.1016\/j.imavis.2026.106045_b16","doi-asserted-by":"crossref","first-page":"4569","DOI":"10.1109\/TITS.2025.3642410","article-title":"Lightweight semantic feature extraction model with direction awareness for aerial traffic object detection","volume":"27","author":"Shen","year":"2026","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"10.1016\/j.imavis.2026.106045_b17","first-page":"1","article-title":"Finger vein recognition algorithm based on lightweight deep convolutional neural network","volume":"71","author":"Shen","year":"2022","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"10.1016\/j.imavis.2026.106045_b18","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2024.109055","article-title":"Monitoring system for peanut leaf disease based on a lightweight deep learning model","volume":"222","author":"Lin","year":"2024","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.imavis.2026.106045_b19","doi-asserted-by":"crossref","DOI":"10.1016\/j.imavis.2024.105052","article-title":"HV-YOLOv8 by HDPconv: Better lightweight detectors for small object detection","volume":"147","author":"Wang","year":"2024","journal-title":"Image Vis. Comput."},{"key":"10.1016\/j.imavis.2026.106045_b20","first-page":"1","article-title":"D-CenterNet: An anchor-free detector with knowledge distillation for industrial defect detection","volume":"71","author":"Liu","year":"2022","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"10.1016\/j.imavis.2026.106045_b21","doi-asserted-by":"crossref","DOI":"10.1016\/j.autcon.2025.106392","article-title":"Lightweight crack detection in complex environments via model pruning and knowledge distillation","volume":"178","author":"Wei","year":"2025","journal-title":"Autom. Constr."},{"key":"10.1016\/j.imavis.2026.106045_b22","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2023.110235","article-title":"Closed-loop unified knowledge distillation for dense object detection","volume":"149","author":"Song","year":"2024","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.imavis.2026.106045_b23","doi-asserted-by":"crossref","unstructured":"Z. Yang, Z. Li, X. Jiang, Y. Gong, Z. Yuan, D. Zhao, C. Yuan, Focal and Global Knowledge Distillation for Detectors, in: Proc. IEEE\/CVF Conf. Comput. Vis. Pattern Recognit., CVPR, 2022, pp. 4633\u20134642.","DOI":"10.1109\/CVPR52688.2022.00460"},{"key":"10.1016\/j.imavis.2026.106045_b24","doi-asserted-by":"crossref","unstructured":"C. Shu, Y. Liu, J. Gao, Z. Yan, C. Shen, Channel-Wise Knowledge Distillation for Dense Prediction, in: Proc. IEEE\/CVF Int. Conf. Comput. Vis., ICCV, 2021, pp. 5311\u20135320.","DOI":"10.1109\/ICCV48922.2021.00526"},{"key":"10.1016\/j.imavis.2026.106045_b25","doi-asserted-by":"crossref","DOI":"10.1016\/j.imavis.2025.105504","article-title":"Part-aware distillation and aggregation network for human parsing","volume":"158","author":"Lai","year":"2025","journal-title":"Image Vis. Comput."},{"issue":"6","key":"10.1016\/j.imavis.2026.106045_b26","doi-asserted-by":"crossref","first-page":"11243","DOI":"10.1109\/TNNLS.2025.3525737","article-title":"DenseKD: Dense knowledge distillation by exploiting region and sample importance","volume":"36","author":"Zhang","year":"2025","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.imavis.2026.106045_b27","doi-asserted-by":"crossref","unstructured":"Z. Zheng, R. Ye, P. Wang, D. Ren, W. Zuo, Q. Hou, M.-M. Cheng, Localization Distillation for Dense Object Detection, in: Proc. IEEE\/CVF Conf. Comput. Vis. Pattern Recognit., CVPR, 2022, pp. 9407\u20139416.","DOI":"10.1109\/CVPR52688.2022.00919"},{"key":"10.1016\/j.imavis.2026.106045_b28","doi-asserted-by":"crossref","unstructured":"J. Wang, Y. Chen, Z. Zheng, X. Li, M.-M. Cheng, Q. Hou, CrossKD: Cross-Head Knowledge Distillation for Object Detection, in: Proc. IEEE\/CVF Conf. Comput. Vis. Pattern Recognit., CVPR, 2024, pp. 16520\u201316530.","DOI":"10.1109\/CVPR52733.2024.01563"},{"key":"10.1016\/j.imavis.2026.106045_b29","first-page":"1","article-title":"LLD-MFCOS: A multiscale anchor-free detector based on label localization distillation for wheelset tread defect detection","volume":"73","author":"Yang","year":"2024","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"10.1016\/j.imavis.2026.106045_b30","doi-asserted-by":"crossref","DOI":"10.1016\/j.imavis.2026.105957","article-title":"CSG-DOF: A class structure-guided discriminative optimization framework for few-shot object detection","volume":"169","author":"Du","year":"2026","journal-title":"Image Vis. Comput."},{"key":"10.1016\/j.imavis.2026.106045_b31","doi-asserted-by":"crossref","first-page":"5984","DOI":"10.1109\/TIP.2021.3089942","article-title":"Delving deep into label smoothing","volume":"30","author":"Zhang","year":"2021","journal-title":"IEEE Trans. Image Process."},{"issue":"7","key":"10.1016\/j.imavis.2026.106045_b32","doi-asserted-by":"crossref","first-page":"5571","DOI":"10.1109\/TPAMI.2025.3554235","article-title":"DIST+: Knowledge distillation from a stronger adaptive teacher","volume":"47","author":"Huang","year":"2025","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"5","key":"10.1016\/j.imavis.2026.106045_b33","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1007\/s00530-024-01483-w","article-title":"ATMKD: Adaptive temperature guided multi-teacher knowledge distillation","volume":"30","author":"Lin","year":"2024","journal-title":"Multimedia Syst."},{"key":"10.1016\/j.imavis.2026.106045_b34","doi-asserted-by":"crossref","unstructured":"Z. Li, X. Li, L.F. Yang, B.R. Zhao, R.J. Song, L. Luo, J. Li, J. Yang, Curriculum Temperature for Knowledge Distillation, in: Proc. AAAI Conf. Artif. Intell., AAAI, 2023, pp. 1504\u20131512.","DOI":"10.1609\/aaai.v37i2.25236"},{"key":"10.1016\/j.imavis.2026.106045_b35","doi-asserted-by":"crossref","unstructured":"S.Q. Sun, W.Q. Ren, J.Z. Li, R. Wang, X.C. Cao, Logit Standardization in Knowledge Distillation, in: Proc. IEEE\/CVF Conf. Comput. Vis. Pattern Recognit., CVPR, 2024, pp. 15731\u201315740.","DOI":"10.1109\/CVPR52733.2024.01489"},{"key":"10.1016\/j.imavis.2026.106045_b36","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2025.129745","article-title":"Adaptive temperature distillation method for mining hard samples\u2019 knowledge","volume":"636","author":"Yang","year":"2025","journal-title":"Neurocomput."},{"key":"10.1016\/j.imavis.2026.106045_b37","doi-asserted-by":"crossref","first-page":"472","DOI":"10.1016\/j.aej.2025.06.013","article-title":"From sigmoid to SoftProb: A novel output activation function for multi-label learning","volume":"129","author":"Alzhrani","year":"2025","journal-title":"Alex. Eng. J."},{"key":"10.1016\/j.imavis.2026.106045_b38","doi-asserted-by":"crossref","unstructured":"C.J. Feng, Y.J. Zhong, Y. Gao, M.R. Scott, W.L. Huang, TOOD: Task-Aligned One-Stage Object Detection, in: Proc. IEEE\/CVF Int. Conf. Comput. Vis., ICCV, 2021, pp. 3490\u20133499.","DOI":"10.1109\/ICCV48922.2021.00349"},{"key":"10.1016\/j.imavis.2026.106045_b39","first-page":"21002","article-title":"Generalized Focal Loss: Learning qualified and distributed bounding boxes for dense object detection","volume":"vol. 33","author":"Li","year":"2020"},{"key":"10.1016\/j.imavis.2026.106045_b40","doi-asserted-by":"crossref","first-page":"617","DOI":"10.1016\/j.neunet.2023.05.006","article-title":"Multi-granularity knowledge distillation and prototype consistency regularization for class-incremental learning","volume":"164","author":"Shi","year":"2023","journal-title":"Neural Netw."},{"key":"10.1016\/j.imavis.2026.106045_b41","doi-asserted-by":"crossref","first-page":"34954","DOI":"10.1109\/ACCESS.2025.3544361","article-title":"Positive Anchor Area merge algorithm: A knowledge distillation algorithm for fruit detection tasks based on YOLOv8","volume":"13","author":"Shi","year":"2025","journal-title":"IEEE Access"},{"key":"10.1016\/j.imavis.2026.106045_b42","doi-asserted-by":"crossref","unstructured":"T.Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Doll\u00e1r, C.L. Zitnick, Microsoft COCO: Common Objects in Context, in: Proc. Eur. Conf. Comput. Vis., ECCV, 2014, pp. 740\u2013755.","DOI":"10.1007\/978-3-319-10602-1_48"},{"issue":"2","key":"10.1016\/j.imavis.2026.106045_b43","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","article-title":"The PASCAL visual object classes (VOC) challenge","volume":"88","author":"Everingham","year":"2010","journal-title":"Int. J. Comput. Vis."},{"key":"10.1016\/j.imavis.2026.106045_b44","doi-asserted-by":"crossref","unstructured":"D. Du, P. Zhu, L. Wen, X. Bian, H. Lin, Q. Hu, T. Peng, J. Zheng, X. Wang, Y. Zhang, L. Bo, H. Shi, R. Zhu, A. Kumar, A. Li, A. Zinollayev, A. Askergaliyev, A. Schumann, B. Mao, et al., VisDrone-DET2019: The Vision Meets Drone Object Detection in Image Challenge Results, in: Proc. IEEE\/CVF Int. Conf. Comput. Vis. Workshops, ICCVW, 2019, pp. 213\u2013226.","DOI":"10.1109\/ICCVW.2019.00030"}],"container-title":["Image and Vision Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0262885626001526?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0262885626001526?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T19:07:08Z","timestamp":1783192028000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0262885626001526"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8]]},"references-count":44,"alternative-id":["S0262885626001526"],"URL":"https:\/\/doi.org\/10.1016\/j.imavis.2026.106045","relation":{},"ISSN":["0262-8856"],"issn-type":[{"value":"0262-8856","type":"print"}],"subject":[],"published":{"date-parts":[[2026,8]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Protocol-consistent Sigmoid- logit distillation for lightweight object detection","name":"articletitle","label":"Article Title"},{"value":"Image and Vision Computing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.imavis.2026.106045","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"106045"}}