{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T04:50:14Z","timestamp":1770353414831,"version":"3.49.0"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,11,6]],"date-time":"2025-11-06T00:00:00Z","timestamp":1762387200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,11,6]],"date-time":"2025-11-06T00:00:00Z","timestamp":1762387200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"Xingdian Talent Support Program Industrial Innovation Talent Project of Yunnan","award":["No.yfgrc202422"],"award-info":[{"award-number":["No.yfgrc202422"]}]},{"name":"Science Foundation of Young and Middle-aged Academic and Technical Leaders of Yunnan","award":["No. 202205AC160040"],"award-info":[{"award-number":["No. 202205AC160040"]}]},{"name":"Science Foundation of Yunnan Jinzhi Expert Workstation","award":["No. 202205AF150006"],"award-info":[{"award-number":["No. 202205AF150006"]}]},{"name":"Major Project of Yunnan Natural Science Foundation","award":["No. 202302AE09002003"],"award-info":[{"award-number":["No. 202302AE09002003"]}]},{"name":"Special funding projects for the national guidance of provincial science and technology development","award":["No. 202407AB110010"],"award-info":[{"award-number":["No. 202407AB110010"]}]},{"name":"Knowledge-driven Smart Energy Science and Technology Innovation Team of Yunnan Provincial Department of Education"},{"name":"Open Foundation of Yunnan Key Laboratory of Software Engineering","award":["No. 2023SE101"],"award-info":[{"award-number":["No. 2023SE101"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Complex Intell. Syst."],"published-print":{"date-parts":[[2026,1]]},"DOI":"10.1007\/s40747-025-02123-0","type":"journal-article","created":{"date-parts":[[2025,11,6]],"date-time":"2025-11-06T03:22:10Z","timestamp":1762399330000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A knowledge distillation method based on detection transformer for farmland obstacle detection"],"prefix":"10.1007","volume":"12","author":[{"given":"Wenjie","family":"Gao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chaojie","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2929-2126","authenticated-orcid":false,"given":"Xuan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"ZhaoHui","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rui","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Ling","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chen","family":"Gao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Cai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tong","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,11,6]]},"reference":[{"issue":"1","key":"2123_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.inpa.2019.09.006","volume":"7","author":"H Tian","year":"2020","unstructured":"Tian H, Wang T, Liu Y, Qiao X, Li Y (2020) Computer vision technology in agricultural automation\u2014a review. Inf Process Agric 7(1):1\u201319. https:\/\/doi.org\/10.1016\/j.inpa.2019.09.006","journal-title":"Inf Process Agric"},{"issue":"1","key":"2123_CR2","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1007\/s40747-024-01749-w","volume":"11","author":"J Zhang","year":"2024","unstructured":"Zhang J, Li D, Shi X, Wang F, Li L, Chen Y (2024) DCTNet: a double-channel transformer network for peach disease detection using UAVs. Complex Intell Syst 11(1):111. https:\/\/doi.org\/10.1007\/s40747-024-01749-w","journal-title":"Complex Intell Syst"},{"issue":"4","key":"2123_CR3","doi-asserted-by":"publisher","first-page":"4227","DOI":"10.1007\/s40747-022-00955-8","volume":"9","author":"J Wang","year":"2022","unstructured":"Wang J, Zhang C, Yan T, Yang J, Lu X, Lu G, Huang B (2022) A cross-domain fruit classification method based on lightweight attention networks and unsupervised domain adaptation. Complex Intell Syst 9(4):4227\u20134247. https:\/\/doi.org\/10.1007\/s40747-022-00955-8","journal-title":"Complex Intell Syst"},{"issue":"15","key":"2123_CR4","doi-asserted-by":"publisher","first-page":"4082","DOI":"10.3390\/s20154082","volume":"20","author":"Z Qiu","year":"2020","unstructured":"Qiu Z, Zhao N, Zhou L, Wang M, Yang L, Fang H, He Y, Liu Y (2020) Vision-based moving obstacle detection and tracking in paddy field using improved YOLOv3 and deep sort. Sensors 20(15):4082. https:\/\/doi.org\/10.3390\/s20154082","journal-title":"Sensors"},{"issue":"1","key":"2123_CR5","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1007\/s10846-020-01284-z","volume":"101","author":"HY Lee","year":"2021","unstructured":"Lee HY, Ho HW, Zhou Y (2021) Deep learning-based monocular obstacle avoidance for unmanned aerial vehicle navigation in tree plantations: faster region-based convolutional neural network approach. J Intell Robot Syst 101(1):5. https:\/\/doi.org\/10.1007\/s10846-020-01284-z","journal-title":"J Intell Robot Syst"},{"issue":"5","key":"2123_CR6","doi-asserted-by":"publisher","first-page":"1790","DOI":"10.3390\/s22051790","volume":"22","author":"J Xue","year":"2022","unstructured":"Xue J, Cheng F, Li Y, Song Y, Mao T (2022) Detection of farmland obstacles based on an improved yolov5s algorithm by using CIoU and anchor box scale clustering. Sensors 22(5):1790. https:\/\/doi.org\/10.3390\/s22051790","journal-title":"Sensors"},{"key":"2123_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2022.107546","volume":"204","author":"Y Yu","year":"2023","unstructured":"Yu Y, Liu Y, Wang J, Noguchi N, He Y (2023) Obstacle avoidance method based on double DQN for agricultural robots. Comput Electron Agric 204:107546. https:\/\/doi.org\/10.1016\/j.compag.2022.107546","journal-title":"Comput Electron Agric"},{"key":"2123_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2020.105932","volume":"181","author":"Y Li","year":"2021","unstructured":"Li Y, Li M, Qi J, Zhou D, Zou Z, Liu K (2021) Detection of typical obstacles in orchards based on deep convolutional neural network. Comput Electron Agric 181:105932. https:\/\/doi.org\/10.1016\/j.compag.2020.105932","journal-title":"Comput Electron Agric"},{"issue":"11","key":"2123_CR9","doi-asserted-by":"publisher","first-page":"2579","DOI":"10.3390\/s17112579","volume":"17","author":"MF Kragh","year":"2017","unstructured":"Kragh MF, Christiansen P, Laursen MS, Larsen M, Steen KA, Green O, Karstoft H, J\u00f8rgensen RN (2017) FieldSAFE: dataset for obstacle detection in agriculture. Sensors 17(11):2579. https:\/\/doi.org\/10.3390\/s17112579","journal-title":"Sensors"},{"key":"2123_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2021.106104","volume":"184","author":"H Xu","year":"2021","unstructured":"Xu H, Li S, Ji Y, Cao R, Zhang M (2021) Dynamic obstacle detection based on panoramic vision in the moving state of agricultural machineries. Comput Electron Agric 184:106104. https:\/\/doi.org\/10.1016\/j.compag.2021.106104","journal-title":"Comput Electron Agric"},{"issue":"16","key":"2123_CR11","doi-asserted-by":"publisher","first-page":"5292","DOI":"10.3390\/s21165292","volume":"21","author":"M Skocze\u0144","year":"2021","unstructured":"Skocze\u0144 M, Ochman M, Spyra K, Nikodem M, Krata D, Panek M, Paw\u0142owski A (2021) Obstacle detection system for agricultural mobile robot application using RGB-D cameras. Sensors 21(16):5292. https:\/\/doi.org\/10.3390\/s21165292","journal-title":"Sensors"},{"key":"2123_CR12","doi-asserted-by":"publisher","unstructured":"Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. Adv Neural Inf Process Syst 27. https:\/\/doi.org\/10.48550\/arXiv.1406.2661","DOI":"10.48550\/arXiv.1406.2661"},{"key":"2123_CR13","doi-asserted-by":"publisher","unstructured":"Zhao WX, Zhou K, Li J, Tang T, Wang X, Hou Y, Min Y, Zhang B, Zhang J, Dong Z et al (2023) A survey of large language models. arXiv preprint arXiv:2303.18223, https:\/\/doi.org\/10.48550\/arXiv.2303.18223","DOI":"10.48550\/arXiv.2303.18223"},{"key":"2123_CR14","doi-asserted-by":"publisher","unstructured":"Croitoru F-A, Hondru V, Ionescu RT, Shah M (2023) Diffusion models in vision: a survey. IEEE Trans Pattern Anal Mach Intell. https:\/\/doi.org\/10.1109\/tpami.2023.3261988","DOI":"10.1109\/tpami.2023.3261988"},{"key":"2123_CR15","doi-asserted-by":"publisher","unstructured":"Cong W, Zhang J, Niu L, Liu L, Ling Z, Li W, Zhang L (2020) Dovenet: Deep image harmonization via domain verification. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 8394\u20138403. https:\/\/doi.org\/10.1109\/cvpr42600.2020.00842","DOI":"10.1109\/cvpr42600.2020.00842"},{"key":"2123_CR16","doi-asserted-by":"publisher","unstructured":"Chen H, Gu Z, Li Y, Lan J, Meng C, Wang W, Li H (2023) Hierarchical dynamic image harmonization. In: Proceedings of the 31st ACM international conference on multimedia, pp 1422\u20131430. https:\/\/doi.org\/10.1145\/3581783.3611747","DOI":"10.1145\/3581783.3611747"},{"key":"2123_CR17","doi-asserted-by":"publisher","unstructured":"Tan L, Li J, Niu L, Zhang L (2023) Deep image harmonization in dual color spaces. In: Proceedings of the 31st ACM international conference on multimedia, pp 2159\u20132167. https:\/\/doi.org\/10.1145\/3581783.3612404","DOI":"10.1145\/3581783.3612404"},{"key":"2123_CR18","doi-asserted-by":"publisher","unstructured":"Efremyan K, Petrova E, Kaskov E, Kapitanov A (2024) PHNet: patch-based normalization for portrait harmonization. arXiv preprint arXiv:2402.17561, https:\/\/doi.org\/10.48550\/arXiv.2402.17561","DOI":"10.48550\/arXiv.2402.17561"},{"key":"2123_CR19","doi-asserted-by":"publisher","unstructured":"Ren S, He K, Girshick R, Sun J (2015) Faster R-CNN: towards real-time object detection with region proposal networks. Adv Neural Inf Process Syst 28. https:\/\/doi.org\/10.1109\/tpami.2016.2577031","DOI":"10.1109\/tpami.2016.2577031"},{"key":"2123_CR20","doi-asserted-by":"publisher","unstructured":"Lin TY, 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. https:\/\/doi.org\/10.1109\/iccv.2017.324","DOI":"10.1109\/iccv.2017.324"},{"key":"2123_CR21","unstructured":"Ge Z, Liu S, Wang F, Li Z, Sun J (2021) YOLOx: Exceeding YOLO series in 2021. arXiv preprint arXiv:2107.08430"},{"key":"2123_CR22","doi-asserted-by":"publisher","unstructured":"Carion N, Massa F, Synnaeve G, Usunier N, Kirillov A, Zagoruyko S (2020) End-to-end object detection with transformers. In: European conference on computer vision. Springer, pp 213\u2013229. https:\/\/doi.org\/10.1007\/978-3-030-58452-8_13","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"2123_CR23","doi-asserted-by":"publisher","unstructured":"Zhang H, Li F, Liu S, Zhang L, Su H, Zhu J, Ni LM, Shum HY (2022) DINO: DETR with improved denoising anchor boxes for end-to-end object detection. arXiv preprint arXiv:2203.03605, https:\/\/doi.org\/10.48550\/arXiv.2203.03605","DOI":"10.48550\/arXiv.2203.03605"},{"key":"2123_CR24","doi-asserted-by":"publisher","unstructured":"Zong Z, Song G, Liu Y (2023) DETRs with collaborative hybrid assignments training. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 6748\u20136758. https:\/\/doi.org\/10.1109\/iccv51070.2023.00621","DOI":"10.1109\/iccv51070.2023.00621"},{"issue":"6","key":"2123_CR25","doi-asserted-by":"publisher","first-page":"1789","DOI":"10.36227\/techrxiv.170422196.66991957\/v1","volume":"129","author":"J Gou","year":"2021","unstructured":"Gou J, Yu B, Maybank SJ, Tao D (2021) Knowledge distillation: a survey. Int J Comput Vis 129(6):1789\u20131819. https:\/\/doi.org\/10.36227\/techrxiv.170422196.66991957\/v1","journal-title":"Int J Comput Vis"},{"key":"2123_CR26","doi-asserted-by":"publisher","unstructured":"Yang Z, Li Z, Jiang X, Gong Y, Yuan Z, Zhao D, Yuan C (2022) Focal and global knowledge distillation for detectors. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 4643\u20134652. https:\/\/doi.org\/10.1109\/cvpr52688.2022.00460","DOI":"10.1109\/cvpr52688.2022.00460"},{"key":"2123_CR27","doi-asserted-by":"publisher","unstructured":"Yang C, Ochal M, Storkey A, Crowley EJ (2022) Prediction-guided distillation for dense object detection. In: European conference on computer vision. Springer, pp 123\u2013138. https:\/\/doi.org\/10.1007\/978-3-031-20077-9_8","DOI":"10.1007\/978-3-031-20077-9_8"},{"key":"2123_CR28","doi-asserted-by":"publisher","unstructured":"Zagoruyko S, Komodakis N (2016) Paying more attention to attention: improving the performance of convolutional neural networks via attention transfer. arXiv preprint arXiv:1612.03928, https:\/\/doi.org\/10.48550\/arXiv.1612.03928","DOI":"10.48550\/arXiv.1612.03928"},{"key":"2123_CR29","doi-asserted-by":"publisher","unstructured":"Romero A, Ballas N, Kahou SE, Chassang A, Gatta C, Bengio Y (2015) FitNets: hints for thin deep nets. https:\/\/doi.org\/10.48550\/arXiv.1412.6550","DOI":"10.48550\/arXiv.1412.6550"},{"key":"2123_CR30","doi-asserted-by":"publisher","unstructured":"Li Q, Jin S, Yan J (2017) Mimicking very efficient network for object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 6356\u20136364. https:\/\/doi.org\/10.1109\/cvpr.2017.776","DOI":"10.1109\/cvpr.2017.776"},{"key":"2123_CR31","doi-asserted-by":"publisher","unstructured":"Guo J, Han K, Wang Y, Wu H, Chen X, Xu C, Xu C (2021) Distilling object detectors via decoupled features. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 2154\u20132164. https:\/\/doi.org\/10.1109\/cvpr46437.2021.00219","DOI":"10.1109\/cvpr46437.2021.00219"},{"key":"2123_CR32","doi-asserted-by":"publisher","unstructured":"Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L (2009) ImageNet: a large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition. IEEE, pp 248\u2013255. https:\/\/doi.org\/10.1109\/cvprw.2009.5206848","DOI":"10.1109\/cvprw.2009.5206848"},{"key":"2123_CR33","doi-asserted-by":"publisher","unstructured":"Kirillov A, Mintun E, Ravi N, Mao H, Rolland C, Gustafson L, Xiao T, Whitehead S, Berg AC, Lo WY et al (2023) Segment anything. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 4015\u20134026. https:\/\/doi.org\/10.1109\/iccv51070.2023.00371","DOI":"10.1109\/iccv51070.2023.00371"},{"key":"2123_CR34","doi-asserted-by":"publisher","unstructured":"Rezatofighi H, Tsoi N, Gwak J, Sadeghian A, Reid I, Savarese S (2019) Generalized intersection over union: a metric and a loss for bounding box regression. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 658\u2013666. https:\/\/doi.org\/10.1109\/cvpr.2019.00075","DOI":"10.1109\/cvpr.2019.00075"},{"key":"2123_CR35","doi-asserted-by":"publisher","unstructured":"Liu S, Zeng Z, Ren T, Li F, Zhang H, Yang J, Li C, Yang J, Su H, Zhu J et al (2023) Grounding DINO: marrying DINO with grounded pre-training for open-set object detection. In: Computer vision\u2014ECCV 2024. https:\/\/doi.org\/10.1007\/978-3-031-72970-6_3","DOI":"10.1007\/978-3-031-72970-6_3"}],"container-title":["Complex & Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-025-02123-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s40747-025-02123-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-025-02123-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T11:48:51Z","timestamp":1769773731000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s40747-025-02123-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,6]]},"references-count":35,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,1]]}},"alternative-id":["2123"],"URL":"https:\/\/doi.org\/10.1007\/s40747-025-02123-0","relation":{},"ISSN":["2199-4536","2198-6053"],"issn-type":[{"value":"2199-4536","type":"print"},{"value":"2198-6053","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,6]]},"assertion":[{"value":"23 January 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 September 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 November 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 that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This work did not involve human participants, animals, or other subjects requiring ethics approval.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"All participants provided explicit consent for their data\/images to be published in this study.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}],"article-number":"6"}}