{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,3]],"date-time":"2025-07-03T04:07:26Z","timestamp":1751515646206,"version":"3.41.0"},"reference-count":30,"publisher":"Springer Science and Business Media LLC","issue":"21","license":[{"start":{"date-parts":[[2024,8,22]],"date-time":"2024-08-22T00:00:00Z","timestamp":1724284800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,8,22]],"date-time":"2024-08-22T00:00:00Z","timestamp":1724284800000},"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":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-024-20071-8","type":"journal-article","created":{"date-parts":[[2024,8,23]],"date-time":"2024-08-23T10:03:32Z","timestamp":1724407412000},"page":"24193-24207","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Field road segmentation method based on two channel feature fusion"],"prefix":"10.1007","volume":"84","author":[{"given":"Wei","family":"Depeng","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Long","family":"Teng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,8,22]]},"reference":[{"issue":"22","key":"20071_CR1","doi-asserted-by":"publisher","first-page":"152","DOI":"10.11975\/j.issn.1002-6819.2021.22.017","volume":"37","author":"Meng Qingkuan","year":"2021","unstructured":"Qingkuan Meng, Xiaoxia Yang, Man Zhang et al (2021) Recognition of unstructured field road scene based on semantic segmentation model[J]. Trans Chinese Soc Agri Eng (Transactions of the CSAE) 37(22):152\u2013160.in Chinese with English abstract.\u00a0https:\/\/doi.org\/10.11975\/j.issn.1002-6819.2021.22.017\u00a0 http:\/\/www.tcsae.org\u00a0","journal-title":"Trans Chinese Soc Agri Eng (Transactions of the CSAE)"},{"issue":"1","key":"20071_CR2","first-page":"1","volume":"51","author":"L Chengliang","year":"2020","unstructured":"Chengliang L, Hongzhen L, Yanming Li et al (2020) Analysis on status and development trend of intelligent control technology for agricultural equipment[J]. Trans Chinese Agric Machinery 51(1):1\u201318 (in Chinese with English abstract)","journal-title":"Trans Chinese Agric Machinery"},{"key":"20071_CR3","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1016\/j.compag.2013.10.012","volume":"100","author":"HS Chattha","year":"2014","unstructured":"Chattha HS, Zaman QU, Chang YK et al (2014) Variable rate spreader for real-time spot-application of granular fertilizer in wild blueberry[J]. Comput Electron Agric 100:70\u201378","journal-title":"Comput Electron Agric"},{"key":"20071_CR4","doi-asserted-by":"publisher","unstructured":"Onishi Y, Yoshida T, Kurita H et al (2019) An automated fruit harvesting robot by using deep learning[J]. ROBOMECH J 6(1). https:\/\/doi.org\/10.1186\/s40648-019-0141-2","DOI":"10.1186\/s40648-019-0141-2"},{"issue":"18","key":"20071_CR5","first-page":"51","volume":"34","author":"C Jianguo","year":"2018","unstructured":"Jianguo C, Yanming Li, Chengjin Q et al (2018) Design and test of capacitive detection system for wheat seeding quantity[J]. Trans Chinese Soc Agri Eng (Transactions of the CSAE) 34(18):51\u201358 (in Chinese with English abstract)","journal-title":"Trans Chinese Soc Agri Eng (Transactions of the CSAE)"},{"issue":"04","key":"20071_CR6","first-page":"1","volume":"51","author":"Z Man","year":"2020","unstructured":"Man Z, Yuhan Ji, Shichao Li, Cao Ruyue Xu, Hongzhen ZZ (2020) Research1 Progress of Agricultural M lachinery Navigation Technology [J]. Trans Chinese Soc Agri Machinery 51(04):1\u201318","journal-title":"Trans Chinese Soc Agri Machinery"},{"key":"20071_CR7","first-page":"599","volume":"2015","author":"T Scharwachter","year":"2015","unstructured":"Scharwachter T, Franke U (2015) Low-level fusion of color, texture and depth for robust road scene understanding[C]\/\/. IEEE In Intelligent Vehicles Symposium (IV) 2015:599\u2013604","journal-title":"IEEE In Intelligent Vehicles Symposium (IV)"},{"issue":"10","key":"20071_CR8","doi-asserted-by":"publisher","first-page":"3906","DOI":"10.1109\/TGRS.2011.2136381","volume":"49","author":"S Das","year":"2011","unstructured":"Das S, Mirnalinee TT, Varghese K (2011) Use of salient Features for the design of a multistage framework to extract roads from high-resolution multispectral satellite images[J]. IEEE Trans Geosci Remote Sens 49(10):3906\u20133931","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"31","key":"20071_CR9","first-page":"263","volume":"19","author":"T Siran","year":"2019","unstructured":"Siran T (2019) Road segmentation of high-spatial resolution remote sensing images by considering gradient and color information [J]. Sci Technol Eng 19(31):263\u2013269","journal-title":"Sci Technol Eng"},{"issue":"4","key":"20071_CR10","doi-asserted-by":"publisher","first-page":"545","DOI":"10.1109\/LGRS.2016.2524025","volume":"13","author":"G Cheng","year":"2016","unstructured":"Cheng G, Zhu F, Xiang S, Pan C (2016) Road centerline extraction via semi-supervised segmentation and multi direction nonmaximum suppression. IEEE Geosci Remote Sens Lett 13(4):545\u2013549","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"20071_CR11","doi-asserted-by":"publisher","first-page":"104906","DOI":"10.1016\/j.compag.2019.104906","volume":"164","author":"K Thenmozhi","year":"2024","unstructured":"Thenmozhi K, Reddy US (2024) Crop pest classification based on deep convolutional neural network and transfer learning - ScienceDirect[J]. Comput Electron Agric 164:104906\u2013104906. https:\/\/doi.org\/10.1016\/j.compag.2019.104906","journal-title":"Comput Electron Agric"},{"key":"20071_CR12","doi-asserted-by":"crossref","unstructured":"Liu S, Huang S, Xu X et al (2023) Efficient visual tracking based on fuzzy inference for intelligent transportation systems[J]. IEEE Trans Intell Transp Syst","DOI":"10.1109\/TITS.2022.3232242"},{"key":"20071_CR13","doi-asserted-by":"publisher","first-page":"105326","DOI":"10.1016\/j.compag.2020.105326","volume":"171","author":"LT Duong","year":"2020","unstructured":"Duong LT, Nguyen PT, Sipio CD et al (2020) Automated fruit recognition using EfficientNet and MixNet[J]. Comput Electron Agric 171:105326","journal-title":"Comput Electron Agric"},{"key":"20071_CR14","doi-asserted-by":"publisher","first-page":"105450","DOI":"10.1016\/j.compag.2020.105450","volume":"174","author":"H Jiang","year":"2020","unstructured":"Jiang H, Zhang C, Qiao Y et al (2020) CNN feature based graph convolutional network for weed and crop recognition in smart farming[J]. Comput Electron Agric 174:105450","journal-title":"Comput Electron Agric"},{"issue":"4","key":"20071_CR15","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1016\/j.jksuci.2023.03.011","volume":"35","author":"X Liu","year":"2023","unstructured":"Liu X, Hou S, Liu S et al (2023) Attention-based multimodal glioma segmentation with multi-attention layers for small-intensity dissimilarity[J]. J King Saud Univ Comput Inform Sci 35(4):183\u2013195","journal-title":"J King Saud Univ Comput Inform Sci"},{"key":"20071_CR16","doi-asserted-by":"publisher","unstructured":"G\u00f3mez O, Mesejo P, Ib\u00e1\u00f1ez O et al (2020) Deep architectures for high-resolution multi-organ chest X-ray image segmentation[J]. Neural Comput Appl 32(2). https:\/\/doi.org\/10.1007\/s00521-019-04532-y","DOI":"10.1007\/s00521-019-04532-y"},{"key":"20071_CR17","doi-asserted-by":"publisher","unstructured":"Zhang M, Li X, Xu M et al (2020) Automated Semantic Segmentation of Red Blood Cells for Sickle Cell Disease[J]. IEEE J Biomed Health Inform (99):1\u20131. https:\/\/doi.org\/10.1109\/JBHI.2020.3000484","DOI":"10.1109\/JBHI.2020.3000484"},{"key":"20071_CR18","doi-asserted-by":"publisher","first-page":"2188","DOI":"10.1109\/TMM.2021.3065580","volume":"23","author":"S Liu","year":"2021","unstructured":"Liu S, Wang S, Liu X et al (2021) Human memory update strategy: a multi-layer template update mechanism for remote visual monitoring[J]. IEEE Trans Multimedia 23:2188\u20132198","journal-title":"IEEE Trans Multimedia"},{"issue":"4","key":"20071_CR19","first-page":"640","volume":"39","author":"J Long","year":"2015","unstructured":"Long J, Shelhamer E, Darrell T (2015) Fully convolutional cetworks for semantic segmentation[J]. IEEE Ransactions Patt Anal Mach Intell 39(4):640\u2013651","journal-title":"IEEE Ransactions Patt Anal Mach Intell"},{"key":"20071_CR20","doi-asserted-by":"publisher","unstructured":"Wang J, Kim J (2017) Semantic segmentation of urban scenes with a location prior using lidar measurements[C]. In: IEEE\/RSJ International Conference on Intelligent Robots & Systems, IEEE, pp 661\u2013666. https:\/\/doi.org\/10.1109\/IROS.2017.8202222","DOI":"10.1109\/IROS.2017.8202222"},{"issue":"99","key":"20071_CR21","first-page":"1","volume":"32","author":"Z Zhang","year":"2017","unstructured":"Zhang Z, Liu Q, Wang Y (2017) Road extraction by deep residual U-Net[J]. IEEE Geosci Remote Sens Lett 32(99):1\u20135","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"20071_CR22","doi-asserted-by":"publisher","unstructured":"He K, Zhang X, Ren S et al (2016) Deep residual learning for image recognition[J]. IEEE. https:\/\/doi.org\/10.1109\/CVPR.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"key":"20071_CR23","doi-asserted-by":"publisher","unstructured":"Chen Z, Chen Z (2017) RBNet: A deep neural network for unified road and road boundary detection[J].\u00a0 https:\/\/doi.org\/10.1007\/978-3-319-70087-8_70","DOI":"10.1007\/978-3-319-70087-8_70"},{"issue":"4","key":"20071_CR24","doi-asserted-by":"publisher","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","volume":"40","author":"LC Chen","year":"2016","unstructured":"Chen LC, Papandreou G, Kokkinos I et al (2016) DeepLab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs[J]. IEEE Trans Pattern Anal Mach Intell 40(4):834\u2013848","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"20071_CR25","doi-asserted-by":"publisher","unstructured":"Chen LC, Papandreou G, Schroff F et al (2017) Rethinking atrous convolution for semantic image segmentation[J]. https:\/\/doi.org\/10.48550\/arxiv.1706.05587","DOI":"10.48550\/arxiv.1706.05587"},{"key":"20071_CR26","doi-asserted-by":"publisher","unstructured":"Chen LC, Zhu Y, Papandreou G et al (2018) Encoder-decoder with atrous separable convolution for semantic image segmentation[C]. In: European Conference on Computer Vision, Springer, Cham.\u00a0 https:\/\/doi.org\/10.1007\/978-3-030-01234-2_49","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"20071_CR27","doi-asserted-by":"publisher","first-page":"430","DOI":"10.1016\/j.patcog.2018.05.016","volume":"83","author":"Z Zhang","year":"2018","unstructured":"Zhang Z, Xu C, Yang J et al (2018) Deep hierarchical guidance and regularization learning for end-to-end depth estimation[J]. Pattern Recogn 83:430\u2013442","journal-title":"Pattern Recogn"},{"key":"20071_CR28","doi-asserted-by":"publisher","first-page":"105174","DOI":"10.1016\/j.compag.2019.105174","volume":"169","author":"Y Li","year":"2020","unstructured":"Li Y, Wang H, Dang LM et al (2020) Crop pest recognition in natural scenes using convolutional neural networks[J]. Comput Electron Agri 169:105174","journal-title":"Comput Electron Agri"},{"issue":"1","key":"20071_CR29","doi-asserted-by":"publisher","first-page":"105834","DOI":"10.1016\/j.compag.2020.105834","volume":"179","author":"J Wang","year":"2020","unstructured":"Wang J, Li Y, Feng H et al (2020) Common pests image recognition based on deep convolutional neural network[J]. Comput Electron Agric 179(1):105834","journal-title":"Comput Electron Agric"},{"key":"20071_CR30","doi-asserted-by":"publisher","unstructured":"Wang Q, Wu B, Zhu P et al (2020) ECA-Net: Efficient channel attention for deep convolutional neural networks[C]. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE. https:\/\/doi.org\/10.1109\/CVPR42600.2020.01155","DOI":"10.1109\/CVPR42600.2020.01155"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-20071-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-024-20071-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-20071-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,2]],"date-time":"2025-07-02T13:58:41Z","timestamp":1751464721000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-024-20071-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,22]]},"references-count":30,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2025,6]]}},"alternative-id":["20071"],"URL":"https:\/\/doi.org\/10.1007\/s11042-024-20071-8","relation":{},"ISSN":["1573-7721"],"issn-type":[{"type":"electronic","value":"1573-7721"}],"subject":[],"published":{"date-parts":[[2024,8,22]]},"assertion":[{"value":"11 August 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 June 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 August 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 August 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"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":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}