{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T04:22:05Z","timestamp":1783398125873,"version":"3.54.6"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,4,14]],"date-time":"2022-04-14T00:00:00Z","timestamp":1649894400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,4,14]],"date-time":"2022-04-14T00:00:00Z","timestamp":1649894400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"the National Science Foundation of China","award":["U1803261"],"award-info":[{"award-number":["U1803261"]}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61966035"],"award-info":[{"award-number":["61966035"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Creative Research Groups of Higher Education of Xinjiang Uygur Autonomous Region","award":["XJEDU2017T002"],"award-info":[{"award-number":["XJEDU2017T002"]}]},{"name":"Autonomous Region Graduate Innovation Project","award":["XJ2019G072"],"award-info":[{"award-number":["XJ2019G072"]}]},{"name":"Tianshan Innovation Team Plan Project of Xinjiang Uygur Autonomous Region","award":["202101642"],"award-info":[{"award-number":["202101642"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2023,1]]},"DOI":"10.1007\/s10489-022-03459-7","type":"journal-article","created":{"date-parts":[[2022,4,14]],"date-time":"2022-04-14T23:02:46Z","timestamp":1649977366000},"page":"238-250","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["MTSDet: multi-scale traffic sign detection with attention and path aggregation"],"prefix":"10.1007","volume":"53","author":[{"given":"Hongyang","family":"Wei","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qianqian","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6564-4745","authenticated-orcid":false,"given":"Yurong","family":"Qian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zheng","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingjing","family":"Han","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,4,14]]},"reference":[{"key":"3459_CR1","doi-asserted-by":"publisher","unstructured":"Malik, Khurshid J, Ahmad SN (2007) Road sign detection and recognition using colour segmentation. In: Shape analysis and template matching, 2007 international conference on machine learning and cybernetics, pp 3556\u20133560, DOI https:\/\/doi.org\/10.1109\/ICMLC.2007.4370763, (to appear in print)","DOI":"10.1109\/ICMLC.2007.4370763"},{"issue":"4","key":"3459_CR2","doi-asserted-by":"publisher","first-page":"675","DOI":"10.1016\/j.jvcir.2005.10.003","volume":"17","author":"XW Gao","year":"2006","unstructured":"Gao XW, Podladchikova L, Shaposhnikov D, et al. (2006) Recognition of traffic signs based on their colour and shape features extracted using human vision models[J]. J Vis Commun Image Represent 17 (4):675\u2013685","journal-title":"J Vis Commun Image Represent"},{"key":"3459_CR3","doi-asserted-by":"publisher","first-page":"805","DOI":"10.1016\/j.asoc.2015.12.041","volume":"46","author":"A Ellahyani","year":"2016","unstructured":"Ellahyani A, Ansari ME, Jaafari IE (2016) Traffic sign detection and recognition based on random forests[J]. Appl Soft Comput 46:805\u2013815","journal-title":"Appl Soft Comput"},{"key":"3459_CR4","first-page":"91","volume":"28","author":"S Ren","year":"2015","unstructured":"Ren S, He K, Girshick R, et al. (2015) Faster r-cnn: towards real-time object detection with region proposal networks[J]. Adv Neural Inform Process Syst 28:91\u201399","journal-title":"Adv Neural Inform Process Syst"},{"key":"3459_CR5","doi-asserted-by":"crossref","unstructured":"He K, Gkioxari G, Doll\u00e1r P et al (2017) Mask r-cnn[C]. In: Proceedings of the IEEE international conference on computer vision, pp 2961\u20132969","DOI":"10.1109\/ICCV.2017.322"},{"key":"3459_CR6","doi-asserted-by":"crossref","unstructured":"Cai Z, Vasconcelos N (2018) Cascade r-cnn. In: Delving into high quality object detection. Proceedings of the IEEE conference on computer vision and pattern recognition","DOI":"10.1109\/CVPR.2018.00644"},{"key":"3459_CR7","unstructured":"Redmon J, Farhadi A (2018) Yolov3: an incremental improvement, arXiv:1804.02767"},{"key":"3459_CR8","doi-asserted-by":"crossref","unstructured":"Lin T-Y, et al. (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision","DOI":"10.1109\/ICCV.2017.324"},{"key":"3459_CR9","volume-title":"Ssd: single shot multibox detector European conference on computer vision","author":"W Liu","year":"2016","unstructured":"Liu W, et al. (2016) Ssd: single shot multibox detector European conference on computer vision. Springer, Cham"},{"key":"3459_CR10","doi-asserted-by":"crossref","unstructured":"Zhu Z, et al. (2016) Traffic-sign detection and classification in the wild. In: Proceedings of the IEEE conference on computer vision and pattern recognition","DOI":"10.1109\/CVPR.2016.232"},{"key":"3459_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10489-019-01511-7","volume":"50","author":"Z Liu","year":"2020","unstructured":"Liu Z, Li D, Ge SS, Tian F (2020) Small traffic sign detection from large image. Appl Intell 50:1\u201313","journal-title":"Appl Intell"},{"key":"3459_CR12","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1016\/j.sysarc.2019.01.012","volume":"97","author":"S Song","year":"2019","unstructured":"Song S, Que Z, Hou J, Du S, Song Y (2019) An efficient convolutional neural network for small traffic sign detection. J Syst Archit 97:269\u2013277","journal-title":"J Syst Archit"},{"key":"3459_CR13","doi-asserted-by":"publisher","first-page":"57120","DOI":"10.1109\/ACCESS.2019.2913882","volume":"7","author":"Z Liu","year":"2019","unstructured":"Liu Z, Du J, Tian F, Wen J (2019) MR-CNN: a multi-scale region-based convolutional neural network for small traffic sign recognition. IEEE Access 7:57120\u201357128","journal-title":"IEEE Access"},{"key":"3459_CR14","doi-asserted-by":"crossref","unstructured":"Roy AG, Navab N, Wachinger C (2018) Concurrent spatial and channel \u2019squeeze & excitation\u2019in fully convolutional networks. In: International conference on medical image computing and computer-assisted intervention. Springer, Cham","DOI":"10.1007\/978-3-030-00928-1_48"},{"key":"3459_CR15","doi-asserted-by":"crossref","unstructured":"Woo S, et al. (2018) Cbam: convolutional block attention module. In: Proceedings of the European conference on computer vision (ECCV)","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"3459_CR16","doi-asserted-by":"crossref","unstructured":"Gao Z, et al. (2019) Global second-order pooling convolutional networks","DOI":"10.1109\/CVPR.2019.00314"},{"key":"3459_CR17","volume-title":"J Zhang","author":"H Xu","year":"2020","unstructured":"Xu H (2020) J Zhang. Adaptive aggregation network for efficient stereo matching. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Aanet"},{"key":"3459_CR18","doi-asserted-by":"publisher","first-page":"29742","DOI":"10.1109\/ACCESS.2020.2972338","volume":"8","author":"J Zhang","year":"2020","unstructured":"Zhang J, et al. (2020) A cascaded R-CNN with multiscale attention and imbalanced samples for traffic sign detection. IEEE Access 8:29742\u201329754","journal-title":"IEEE Access"},{"key":"3459_CR19","doi-asserted-by":"crossref","unstructured":"Liu F, Qian Y, Li H, et al. (2021) CAFFNet: channel attention and feature fusion network for multi-target traffic sign detection[J]. International Journal of Pattern Recognition and Artificial Intelligence","DOI":"10.1142\/S021800142152008X"},{"issue":"12","key":"3459_CR20","doi-asserted-by":"publisher","first-page":"4272","DOI":"10.3390\/s18124272","volume":"18","author":"J Sang","year":"2018","unstructured":"Sang J, Wu Z, Guo P, et al. (2018) An improved YOLOv2 for vehicle detection[J]. Sensors 18(12):4272","journal-title":"Sensors"},{"key":"3459_CR21","first-page":"126","volume-title":"YOLOv3: an incremental improvement. Computer Vision and Pattern Recognition (CVPR)","author":"J Redmon","year":"2018","unstructured":"Redmon J, Farhadi A (2018) YOLOv3: an incremental improvement. Computer Vision and Pattern Recognition (CVPR). IEEE, Salt Lake City, pp 126\u2013134"},{"key":"3459_CR22","doi-asserted-by":"crossref","unstructured":"Liu W, et al. (2016) SSD: single shot multibox detector. In: European conf. computer vision ECCV. Springer, Cham, pp 21\u201337","DOI":"10.1007\/978-3-319-46448-0_2"},{"issue":"6","key":"3459_CR23","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","volume":"39","author":"S Ren","year":"2015","unstructured":"Ren S, et al. (2015) Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell 39(6):1137\u20131149","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"3459_CR24","doi-asserted-by":"crossref","unstructured":"Lin TY, et al. (2017) Focal loss for dense object detection. In: Proc. IEEE Int. conf. computer vision ICCV, Venice, pp 2980\u20132988","DOI":"10.1109\/ICCV.2017.324"},{"key":"3459_CR25","doi-asserted-by":"publisher","first-page":"29742","DOI":"10.1109\/ACCESS.2020.2972338","volume":"8","author":"J Zhang","year":"2020","unstructured":"Zhang J, et al. (2020) A cascaded R-CNN with multiscale attention and imbalanced samples for traffic sign detection. IEEE Access 8:29742\u201329754","journal-title":"IEEE Access"},{"key":"3459_CR26","doi-asserted-by":"crossref","unstructured":"Sun K, Xiao B, Liu D, et al. (2019) Deep high-resolution representation learning for human pose estimation[C]. In: 2019 IEEE\/CVF conference on computer vision and pattern recognition (CVPR). arXiv","DOI":"10.1109\/CVPR.2019.00584"},{"key":"3459_CR27","doi-asserted-by":"crossref","unstructured":"Carion N, Massa F, Synnaeve G et al (2020) End-to-end object detection with transformers[M]","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"3459_CR28","doi-asserted-by":"crossref","unstructured":"Pang J, Chen K, Shi J et al (2020) Libra R-CNN: towards balanced learning for object detection[C]. In: 2019 IEEE\/CVF conference on computer vision and pattern recognition (CVPR). IEEE","DOI":"10.1109\/CVPR.2019.00091"},{"key":"3459_CR29","doi-asserted-by":"crossref","unstructured":"Wu Y, Chen Y, Yuan L et al (2020) Rethinking classification and localization for object detection[C]. In: 2020 IEEE\/CVF conference on computer vision and pattern recognition (CVPR). IEEE","DOI":"10.1109\/CVPR42600.2020.01020"},{"key":"3459_CR30","doi-asserted-by":"crossref","unstructured":"Zhu X, Cheng D, Zhang Z et al (2019) An empirical study of spatial attention mechanisms in deep networks[C]. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 6688\u20136697","DOI":"10.1109\/ICCV.2019.00679"},{"issue":"2-3","key":"3459_CR31","first-page":"708","volume":"235","author":"BB Fan","year":"2021","unstructured":"Fan BB, Yang H (2021) Multi-scale traffic sign detection model with attention[J]. Proceedings of the Institution of Mechanical Engineers Part D: Journal of Automobile Engineering 235(2-3):708\u2013720","journal-title":"Proceedings of the Institution of Mechanical Engineers Part D: Journal of Automobile Engineering"},{"key":"3459_CR32","doi-asserted-by":"crossref","unstructured":"Lopez-Montiel M, Orozco-Rosas U, S\u00e1nchez-Adame M et al (2021) Evaluation method of deep learning-based embedded systems for traffic sign detection[J]. IEEE Access","DOI":"10.1109\/ACCESS.2021.3097969"},{"key":"3459_CR33","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.neucom.2021.04.083","volume":"452","author":"L Shen","year":"2021","unstructured":"Shen L, You L, Peng B, et al. (2021) Group multi-scale attention pyramid network for traffic sign detection[J]. Neurocomputing 452:1\u201314","journal-title":"Neurocomputing"},{"key":"3459_CR34","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1016\/j.neucom.2021.03.049","volume":"447","author":"Y Liu","year":"2021","unstructured":"Liu Y, Peng J, Xue JH, et al. (2021) TSingNet: scale-aware and context-rich feature learning for traffic sign detection and recognition in the wild[J]. Neurocomputing 447:10\u201322","journal-title":"Neurocomputing"},{"key":"3459_CR35","doi-asserted-by":"crossref","unstructured":"Ahmed S, Kamal U, Hasan M K (2021) DFR-TSD: a deep learning based framework for robust traffic sign detection under challenging weather conditions[J]. IEEE Transactions on Intelligent Transportation Systems","DOI":"10.1109\/TITS.2020.3048878"},{"key":"3459_CR36","doi-asserted-by":"crossref","unstructured":"Sudha M (2021) Traffic sign detection and recognition using RGSM and a novel feature extraction method[J]. Peer-to-Peer Networking and Applications, 1\u201312","DOI":"10.1007\/s12083-022-01439-9"},{"key":"3459_CR37","doi-asserted-by":"publisher","first-page":"29742","DOI":"10.1109\/ACCESS.2020.2972338","volume":"8","author":"J Zhang","year":"2020","unstructured":"Zhang J, Xie Z, Sun J, et al. (2020) A cascaded R-CNN with multiscale attention and imbalanced samples for traffic sign detection[J]. IEEE Access 8:29742\u201329754","journal-title":"IEEE Access"},{"issue":"12","key":"3459_CR38","doi-asserted-by":"publisher","first-page":"1712","DOI":"10.1049\/iet-its.2020.0217","volume":"14","author":"Z Liu","year":"2020","unstructured":"Liu Z, Shen C, Fan X, et al. (2020) Scale-aware limited deformable convolutional neural networks for traffic sign detection and classification[J]. IET Intell Transp Syst 14(12):1712\u20131722","journal-title":"IET Intell Transp Syst"},{"issue":"1","key":"3459_CR39","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10489-019-01511-7","volume":"50","author":"Z Liu","year":"2020","unstructured":"Liu Z, Li D, Ge SS, et al. (2020) Small traffic sign detection from large image[J]. Appl Intell 50(1):1\u201313","journal-title":"Appl Intell"},{"issue":"03","key":"3459_CR40","doi-asserted-by":"publisher","first-page":"522","DOI":"10.1109\/TLA.2020.9082723","volume":"18","author":"DC Santos","year":"2020","unstructured":"Santos DC, da Silva FA, Pereira DR, et al. (2020) Real-time traffic sign detection and recognition using CNN[J]. IEEE Lat Am Trans 18(03):522\u2013529","journal-title":"IEEE Lat Am Trans"},{"issue":"5","key":"3459_CR41","doi-asserted-by":"publisher","first-page":"939","DOI":"10.1049\/iet-ipr.2019.0634","volume":"14","author":"A Hechri","year":"2020","unstructured":"Hechri A, Mtibaa A (2020) Two-stage traffic sign detection and recognition based on SVM and convolutional neural networks[J]. IET Image Process 14(5):939\u2013946","journal-title":"IET Image Process"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-022-03459-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-022-03459-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-022-03459-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,3]],"date-time":"2023-01-03T04:37:25Z","timestamp":1672720645000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-022-03459-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,14]]},"references-count":41,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2023,1]]}},"alternative-id":["3459"],"URL":"https:\/\/doi.org\/10.1007\/s10489-022-03459-7","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,14]]},"assertion":[{"value":"2 March 2022","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 April 2022","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}