{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T21:09:09Z","timestamp":1768511349888,"version":"3.49.0"},"reference-count":45,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2025,1,25]],"date-time":"2025-01-25T00:00:00Z","timestamp":1737763200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,25]],"date-time":"2025-01-25T00:00:00Z","timestamp":1737763200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100004731","name":"Natural Science Foundation of Zhejiang Province","doi-asserted-by":"publisher","award":["LY22F030014"],"award-info":[{"award-number":["LY22F030014"]}],"id":[{"id":"10.13039\/501100004731","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61803339"],"award-info":[{"award-number":["61803339"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Fundamental Research Funds of Zhejiang Sci-Tech University","award":["23222115-Y"],"award-info":[{"award-number":["23222115-Y"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Syst Assur Eng Manag"],"published-print":{"date-parts":[[2025,3]]},"DOI":"10.1007\/s13198-025-02719-2","type":"journal-article","created":{"date-parts":[[2025,1,25]],"date-time":"2025-01-25T04:09:17Z","timestamp":1737778157000},"page":"1058-1071","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Lightweight container number recognition based on deep learning"],"prefix":"10.1007","volume":"16","author":[{"given":"Tao","family":"Liu","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0623-6180","authenticated-orcid":false,"given":"Xianqing","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Fang","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,1,25]]},"reference":[{"issue":"3","key":"2719_CR1","doi-asserted-by":"crossref","first-page":"516","DOI":"10.3390\/rs13030516","volume":"13","author":"Y Bazi","year":"2021","unstructured":"Bazi Y, Bashmal L, Rahhal MM et al (2021) Vision transformers for remote sensing image classification. Remote Sens 13(3):516","journal-title":"Remote Sens"},{"issue":"12","key":"2719_CR2","doi-asserted-by":"crossref","first-page":"12897","DOI":"10.3934\/mbe.2022602","volume":"19","author":"M Cao","year":"2022","unstructured":"Cao M, Fu H, Zhu J et al (2022) Lightweight tea bud recognition network integrating ghostnet and yolov5. Math Biosci Eng MBE 19(12):12897\u201312914","journal-title":"Math Biosci Eng MBE"},{"key":"2719_CR3","doi-asserted-by":"crossref","unstructured":"Han K, Wang Y, Tian Q, et\u00a0al (2020) Ghostnet: more features from cheap operations. In: Proceedings of the 2020 IEEE\/CVF conference on computer vision and pattern recognition, pp 1577\u20131586","DOI":"10.1109\/CVPR42600.2020.00165"},{"key":"2719_CR4","doi-asserted-by":"crossref","unstructured":"Howard A, Sandler M, Chu G, et\u00a0al (2019) Searching for mobilenetv3. In: Proceedings of the 2018 IEEE\/CVF conference on computer vision and pattern recognition, pp 1314\u20131324","DOI":"10.1109\/ICCV.2019.00140"},{"key":"2719_CR5","volume":"185","author":"X Hu","year":"2021","unstructured":"Hu X, Liu Y, Zhao Z et al (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":"2719_CR6","doi-asserted-by":"crossref","unstructured":"Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In: Proceedings of the 2018 IEEE\/CVF conference on computer vision and pattern recognition, pp 7132\u20137141","DOI":"10.1109\/CVPR.2018.00745"},{"key":"2719_CR7","volume":"12","author":"B Jin","year":"2021","unstructured":"Jin B, Xu X (2021) House price forecasting with neural networks. Intell Syst Appl 12:200052","journal-title":"Intell Syst Appl"},{"issue":"15","key":"2719_CR8","doi-asserted-by":"crossref","first-page":"8693","DOI":"10.1007\/s00521-024-09531-2","volume":"36","author":"B Jin","year":"2024","unstructured":"Jin B, Xu X (2024) Forecasting wholesale prices of yellow corn through the gaussian process regression. Neural Comput Appl 36(15):8693\u20138710","journal-title":"Neural Comput Appl"},{"issue":"1","key":"2719_CR9","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1007\/s42824-024-00123-y","volume":"6","author":"B Jin","year":"2024","unstructured":"Jin B, Xu X (2024) Palladium price predictions via machine learning. Mater Circ Econ 6(1):32","journal-title":"Mater Circ Econ"},{"issue":"33","key":"2719_CR10","doi-asserted-by":"crossref","first-page":"20863","DOI":"10.1007\/s00521-024-10270-7","volume":"36","author":"B Jin","year":"2024","unstructured":"Jin B, Xu X (2024) Predictions of steel price indices through machine learning for the regional northeast chinese market. Neural Comput Appl 36(33):20863\u201320882","journal-title":"Neural Comput Appl"},{"issue":"1","key":"2719_CR11","volume":"1","author":"B Jin","year":"2024","unstructured":"Jin B, Xu X (2024) Price forecasting through neural networks for crude oil, heating oil, and natural gas. Meas Energy 1(1):100001","journal-title":"Meas Energy"},{"issue":"3","key":"2719_CR12","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1504\/IJMME.2024.140697","volume":"15","author":"B Jin","year":"2024","unstructured":"Jin B, Xu X (2024) Regional steel price index predictions for north china through machine learning. Int J Min Miner Eng 15(3):314\u2013350","journal-title":"Int J Min Miner Eng"},{"issue":"1","key":"2719_CR13","doi-asserted-by":"crossref","first-page":"3491","DOI":"10.18282\/gfr.v6i1.3491","volume":"6","author":"B Jin","year":"2024","unstructured":"Jin B, Xu X (2024) Carbon emission allowance price forecasting for china guangdong carbon emission exchange via the neural network. Glob Finance Rev 6(1):3491","journal-title":"Glob Finance Rev"},{"key":"2719_CR14","doi-asserted-by":"crossref","unstructured":"Jin Y, Wen Y, Liang J (2020) Embedded real-time pedestrian detection system using yolo optimized by lnn. In: Proceedings of the 2020 international conference on electrical, communication, and computer engineering, pp 1\u20135","DOI":"10.1109\/ICECCE49384.2020.9179384"},{"key":"2719_CR15","doi-asserted-by":"crossref","unstructured":"Jin B, Xu X (2024b) Contemporaneous causality among price indices of ten major steel products. Ironmaking & Steelmaking p 03019233241249361","DOI":"10.1177\/03019233241249361"},{"key":"2719_CR16","doi-asserted-by":"crossref","unstructured":"Jin B, Xu X (2024d) Gaussian process regression based silver price forecasts. J Uncertain Syst","DOI":"10.1142\/S1752890924500132"},{"key":"2719_CR17","doi-asserted-by":"crossref","unstructured":"Jin B, Xu X (2024e) Machine learning predictions of regional steel price indices for east china. Ironmaking & Steelmaking p 03019233241254891","DOI":"10.1177\/03019233241254891"},{"key":"2719_CR18","doi-asserted-by":"crossref","unstructured":"Jin B, Xu X (2024f) Machine learning price index forecasts of flat steel products. Miner Econ, 1\u201312","DOI":"10.1007\/s13563-024-00457-8"},{"key":"2719_CR19","doi-asserted-by":"crossref","unstructured":"Jin B, Xu X (2024h) Pre-owned housing price index forecasts using gaussian process regressions. J Model Manage","DOI":"10.1142\/S1752890925500072"},{"key":"2719_CR20","doi-asserted-by":"crossref","unstructured":"Jin B, Xu X (2024l) Wholesale price forecasts of green grams using the neural network. Asian J Econ Bank, 2615\u20139821","DOI":"10.1108\/AJEB-01-2024-0007"},{"key":"2719_CR21","doi-asserted-by":"crossref","unstructured":"Jin B, Xu X, Zhang Y (2024) Thermal coal futures trading volume predictions through the neural network. J Model Manage","DOI":"10.1108\/JM2-09-2023-0207"},{"key":"2719_CR22","volume":"211","author":"J Li","year":"2023","unstructured":"Li J, Li J, Zhao X et al (2023) Lightweight detection networks for tea bud on complex agricultural environment via improved yolov4. Comput Electron Agric 211:107955","journal-title":"Comput Electron Agric"},{"key":"2719_CR23","doi-asserted-by":"crossref","unstructured":"Li C, Liu S, Xia Q, et\u00a0al (2019a) Automatic container code localization and recognition via an efficient code detector and sequence recognition. In: Proceedings of the 2019 IEEE\/ASME international conference on advanced intelligent mechatronics, pp 532\u2013537","DOI":"10.1109\/AIM.2019.8868819"},{"key":"2719_CR24","doi-asserted-by":"crossref","unstructured":"Liu Y, Li T, Jiang L, et\u00a0al (2018b) Container-code recognition system based on computer vision and deep neural networks. In: AIP conference proceedings, pp 20\u201321","DOI":"10.1063\/1.5033782"},{"key":"2719_CR25","doi-asserted-by":"crossref","unstructured":"Liu S, Qi L, Qin H, et\u00a0al (2018a) Path aggregation network for instance segmentation. In: Proceedings of the 2018 IEEE\/CVF conference on computer vision and pattern recognition, pp 8759\u20138768","DOI":"10.1109\/CVPR.2018.00913"},{"key":"2719_CR26","doi-asserted-by":"crossref","unstructured":"Li X, Zhong Z, Wu J, et\u00a0al (2019b) Expectation-maximization attention networks for semantic segmentation. In: Proceedings of the 2019 IEEE\/CVF conference on computer vision and pattern recognition, pp 9167\u20139176","DOI":"10.1109\/ICCV.2019.00926"},{"issue":"16","key":"2719_CR27","doi-asserted-by":"crossref","first-page":"2586","DOI":"10.3390\/electronics11162586","volume":"11","author":"Q Luo","year":"2022","unstructured":"Luo Q, Wang J, Gao M et al (2022) Multiple mechanisms to strengthen the ability of yolov5s for real-time identification of vehicle type. Electronics 11(16):2586","journal-title":"Electronics"},{"key":"2719_CR28","doi-asserted-by":"crossref","unstructured":"Ma N, Zhang X, Zheng HT, et\u00a0al (2018) Shufflenet v2: Practical guidelines for efficient cnn architecture design. In: Proceedings of the European conference on computer vision (ECCV), pp 116\u2013131","DOI":"10.1007\/978-3-030-01264-9_8"},{"key":"2719_CR29","doi-asserted-by":"crossref","unstructured":"Roeksukrungrueang C, Kusonthammrat T, Kunapronsujarit N, et\u00a0al (2018) An implementation of automatic container number recognition system. In: 2018 international workshop on advanced image technology (IWAIT), pp 1\u20134","DOI":"10.1109\/IWAIT.2018.8369807"},{"key":"2719_CR30","doi-asserted-by":"crossref","unstructured":"Sandler M, Howard A, Zhu M, et\u00a0al (2018) Mobilenetv2: inverted residuals and linear bottlenecks. In: Proceedings of the 2018 IEEE\/CVF conference on computer vision and pattern recognition, pp 4510\u20134520","DOI":"10.1109\/CVPR.2018.00474"},{"key":"2719_CR31","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1007\/s10462-020-09854-1","volume":"54","author":"SA Taghanaki","year":"2021","unstructured":"Taghanaki SA, Abhishek K, Cohen JP et al (2021) Deep semantic segmentation of natural and medical images: a review. Artif Intell Rev 54:137\u2013178","journal-title":"Artif Intell Rev"},{"key":"2719_CR32","doi-asserted-by":"crossref","unstructured":"Wang CY, Bochkovskiy A, Liao HYM (2023) Yolov7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. In: Proceedings of the 2023 IEEE\/CVF conference on computer vision and pattern recognition, pp 7464\u20137475","DOI":"10.1109\/CVPR52729.2023.00721"},{"issue":"14","key":"2719_CR33","doi-asserted-by":"crossref","first-page":"3805","DOI":"10.1049\/ipr2.12595","volume":"16","author":"L Wang","year":"2022","unstructured":"Wang L, Ni Q, Chen C et al (2022) Lightweight target detection algorithm based on improved yolov4. IET Image Proc 16(14):3805\u20133813","journal-title":"IET Image Proc"},{"key":"2719_CR34","doi-asserted-by":"crossref","unstructured":"Wang Z, Wang Y (2020) Frca: high-efficiency container number detection and recognition algorithm with enhanced attention. In: Eleventh international conference on graphics and image processing, pp 35\u201345","DOI":"10.1117\/12.2557197"},{"key":"2719_CR35","doi-asserted-by":"crossref","unstructured":"Wang Z, Wang W, Xing Y (2019) Automatic container code recognition via faster-rcnn. In: 2019 5th international conference on control, automation and robotics, pp 870\u2013874","DOI":"10.1109\/ICCAR.2019.8813401"},{"key":"2719_CR36","doi-asserted-by":"crossref","unstructured":"Wang Q, Wu B, Zhu P, et\u00a0al (2020) Eca-net: Efficient channel attention for deep convolutional neural networks. In: Proceedings of the 2020 IEEE\/CVF conference on computer vision and pattern recognition, pp 11531\u201311539","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"2719_CR37","doi-asserted-by":"crossref","unstructured":"Wan Z, Liu Q, Liu T (2021) Multichannel real-time video container numbers recogntion in container yard. In: 2021 China automation congress (CAC), pp 583\u2013588","DOI":"10.1109\/CAC53003.2021.9728242"},{"issue":"9","key":"2719_CR38","doi-asserted-by":"crossref","first-page":"4335","DOI":"10.3390\/s23094335","volume":"23","author":"C Wei","year":"2023","unstructured":"Wei C, Tan Z, Qing Q et al (2023) Fast helmet and license plate detection based on lightweight yolov5. Sensors 23(9):4335","journal-title":"Sensors"},{"key":"2719_CR39","doi-asserted-by":"crossref","unstructured":"Woo S, Park J, Lee JY, et\u00a0al (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":"2719_CR40","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.neucom.2020.01.085","volume":"396","author":"X Wu","year":"2020","unstructured":"Wu X, Sahoo D, Hoi S (2020) Recent advances in deep learning for object detection. Neurocomputing 396:39\u201364","journal-title":"Neurocomputing"},{"issue":"3","key":"2719_CR41","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1002\/isaf.1519","volume":"29","author":"X Xu","year":"2022","unstructured":"Xu X, Zhang Y (2022) Commodity price forecasting via neural networks for coffee, corn, cotton, oats, soybeans, soybean oil, sugar, and wheat. Intell Syst Account Finance Manage 29(3):169\u2013181","journal-title":"Intell Syst Account Finance Manage"},{"issue":"3","key":"2719_CR42","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1080\/09599916.2021.1996446","volume":"39","author":"X Xu","year":"2022","unstructured":"Xu X, Zhang Y (2022) Second-hand house price index forecasting with neural networks. J Prop Res 39(3):215\u2013236","journal-title":"J Prop Res"},{"key":"2719_CR43","unstructured":"Yang L, Zhang RY, Li L, et\u00a0al (2021) Simam: a simple, parameter-free attention module for convolutional neural networks. In: Proceedings of the 38th international conference on machine learning, pp 11863\u201311874"},{"key":"2719_CR44","doi-asserted-by":"crossref","unstructured":"Zhang R, Bahrami Z, Wang T, et\u00a0al (2020) An adaptive deep learning framework for shipping container code localization and recognition. In: IEEE transactions on instrumentation and measurement, pp 1\u201313","DOI":"10.1109\/TIM.2020.3016108"},{"key":"2719_CR45","doi-asserted-by":"crossref","unstructured":"Zhou X, Wang Y, Xiao C, et\u00a0al (2019) Automated visual inspection of glass bottle bottom with saliency detection and template matching. In: IEEE transactions on instrumentation and measurement, pp 4253\u20134267","DOI":"10.1109\/TIM.2018.2886977"}],"container-title":["International Journal of System Assurance Engineering and Management"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13198-025-02719-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13198-025-02719-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13198-025-02719-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,5]],"date-time":"2025-04-05T03:10:17Z","timestamp":1743822617000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13198-025-02719-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,25]]},"references-count":45,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2025,3]]}},"alternative-id":["2719"],"URL":"https:\/\/doi.org\/10.1007\/s13198-025-02719-2","relation":{},"ISSN":["0975-6809","0976-4348"],"issn-type":[{"value":"0975-6809","type":"print"},{"value":"0976-4348","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,25]]},"assertion":[{"value":"14 April 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 December 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 January 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 January 2025","order":4,"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 Conflict of interest to this work.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}