{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T16:43:19Z","timestamp":1779208999688,"version":"3.51.4"},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2025,1,29]],"date-time":"2025-01-29T00:00:00Z","timestamp":1738108800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,29]],"date-time":"2025-01-29T00:00:00Z","timestamp":1738108800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62373311"],"award-info":[{"award-number":["62373311"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62103342"],"award-info":[{"award-number":["62103342"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62106208"],"award-info":[{"award-number":["62106208"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100018542","name":"Natural Science Foundation of Sichuan Province","doi-asserted-by":"crossref","award":["2022NSFSC089"],"award-info":[{"award-number":["2022NSFSC089"]}],"id":[{"id":"10.13039\/501100018542","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100018542","name":"Natural Science Foundation of Sichuan Province","doi-asserted-by":"crossref","award":["2023NSFSC1418"],"award-info":[{"award-number":["2023NSFSC1418"]}],"id":[{"id":"10.13039\/501100018542","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2021TQ0272"],"award-info":[{"award-number":["2021TQ0272"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2021M702715"],"award-info":[{"award-number":["2021M702715"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2025,4]]},"DOI":"10.1007\/s10489-024-06146-x","type":"journal-article","created":{"date-parts":[[2025,1,29]],"date-time":"2025-01-29T07:44:57Z","timestamp":1738136697000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Improving vehicle detection accuracy in complex traffic scenes through context attention and multi-scale feature fusion module"],"prefix":"10.1007","volume":"55","author":[{"given":"Wenbo","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Binglin","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuxin","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tao","family":"Deng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fei","family":"Yan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,1,29]]},"reference":[{"issue":"5","key":"6146_CR1","first-page":"946","volume":"67","author":"W Zhou","year":"2020","unstructured":"Zhou W, Gao S, Zhang L et al (2020) Histogram of oriented gradients feature extraction from raw bayer pattern images. IEEE Trans Circ Syst II: Express Briefs 67(5):946\u2013950","journal-title":"IEEE Trans Circ Syst II: Express Briefs"},{"issue":"4","key":"6146_CR2","doi-asserted-by":"publisher","first-page":"3244","DOI":"10.1109\/TGRS.2020.3008609","volume":"59","author":"S Chen","year":"2020","unstructured":"Chen S, Zhong S, Xue B et al (2020) Iterative scale-invariant feature transform for remote sensing image registration. IEEE Trans Geosci Remote Sens 59(4):3244\u20133265","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"6146_CR3","doi-asserted-by":"crossref","unstructured":"Fuhl W, Schneider J, Kasneci E (2021) 1000 pupil segmentations in a second using haar like features and statistical learning. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 3466\u20133476","DOI":"10.1109\/ICCVW54120.2021.00386"},{"key":"6146_CR4","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 IEEE\/CVF conference on computer vision and pattern recognition (CVPR)","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"6146_CR5","doi-asserted-by":"crossref","unstructured":"Fang P, Zhou J, Roy SK, et\u00a0al (2019) Bilinear attention networks for person retrieval. In: Proceedings of the IEEE\/CVF international conference on computer vision (ICCV)","DOI":"10.1109\/ICCV.2019.00812"},{"key":"6146_CR6","doi-asserted-by":"crossref","unstructured":"Liu Z, Wang L, Wu W, et\u00a0al (2021) Tam: temporal adaptive module for video recognition. In: Proceedings of the IEEE\/CVF international conference on computer vision (ICCV), pp 13,708\u201313,718","DOI":"10.1109\/ICCV48922.2021.01345"},{"key":"6146_CR7","doi-asserted-by":"publisher","unstructured":"Han H, Zhang M, Hou M, et\u00a0al (2020) Stgcn: a spatial-temporal aware graph learning method for poi recommendation. In: 2020 IEEE international conference on data mining (ICDM), pp 1052\u20131057. https:\/\/doi.org\/10.1109\/ICDM50108.2020.00124","DOI":"10.1109\/ICDM50108.2020.00124"},{"key":"6146_CR8","doi-asserted-by":"publisher","unstructured":"Hou Q, Zhou D, Feng J (2021) Coordinate attention for efficient mobile network design. In: IEEE conference on computer vision and pattern recognition, CVPR 2021, virtual, June 19-25, 2021. Computer Vision Foundation \/ IEEE, pp 13,713\u201313,722. https:\/\/doi.org\/10.1109\/CVPR46437.2021.01350https:\/\/openaccess.thecvf.com\/content\/CVPR2021\/html\/Hou_Coordinate_Attention_for_Efficient_Mobile_Network_Design_CVPR_2021_paper.html","DOI":"10.1109\/CVPR46437.2021.01350"},{"key":"6146_CR9","doi-asserted-by":"publisher","unstructured":"Fu J, Liu J, Tian H, et\u00a0al (2019) Dual attention network for scene segmentation. In: IEEE conference on computer vision and pattern recognition, CVPR 2019, Long Beach, CA, USA, June 16-20, 2019. Computer Vision Foundation \/ IEEE, pp 3146\u20133154. https:\/\/doi.org\/10.1109\/CVPR.2019.00326http:\/\/openaccess.thecvf.com\/content_CVPR_2019\/html\/Fu_Dual_Attention_Network_for_Scene_Segmentation_CVPR_2019_paper.html","DOI":"10.1109\/CVPR.2019.00326"},{"key":"6146_CR10","doi-asserted-by":"publisher","unstructured":"Cai X, Lai Q, Wang Y, et\u00a0al (2024) Poly kernel inception network for remote sensing detection. In: IEEE\/CVF conference on computer vision and pattern recognition, CVPR 2024, Seattle, WA, USA, June 16-22, 2024. IEEE, pp 27,706\u201327,716. https:\/\/doi.org\/10.1109\/CVPR52733.2024.02617","DOI":"10.1109\/CVPR52733.2024.02617"},{"key":"6146_CR11","doi-asserted-by":"crossref","unstructured":"Zhao H, Shi J, Qi X, et\u00a0al (2017) Pyramid scene parsing network. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2881\u20132890","DOI":"10.1109\/CVPR.2017.660"},{"issue":"3","key":"6146_CR12","doi-asserted-by":"publisher","first-page":"3448","DOI":"10.1109\/TITS.2022.3228042","volume":"24","author":"H Pan","year":"2022","unstructured":"Pan H, Hong Y, Sun W et al (2022) Deep dual-resolution networks for real-time and accurate semantic segmentation of traffic scenes. IEEE Trans Intell Transp Syst 24(3):3448\u20133460","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"6146_CR13","doi-asserted-by":"crossref","unstructured":"He J, Deng Z, Zhou L, et\u00a0al (2019) Adaptive pyramid context network for semantic segmentation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 7519\u20137528","DOI":"10.1109\/CVPR.2019.00770"},{"key":"6146_CR14","doi-asserted-by":"crossref","unstructured":"Hou Q, Zhang L, Cheng MM, et\u00a0al (2020) Strip pooling: rethinking spatial pooling for scene parsing. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 4003\u20134012","DOI":"10.1109\/CVPR42600.2020.00406"},{"key":"6146_CR15","doi-asserted-by":"crossref","unstructured":"Xu J, Xiong Z, Bhattacharyya SP (2023) Pidnet: a real-time semantic segmentation network inspired by pid controllers. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 19,529\u201319,539","DOI":"10.1109\/CVPR52729.2023.01871"},{"issue":"8","key":"6146_CR16","doi-asserted-by":"publisher","first-page":"8574","DOI":"10.1109\/TCYB.2021.3095305","volume":"52","author":"Z Zheng","year":"2022","unstructured":"Zheng Z, Wang P, Ren D et al (2022) Enhancing geometric factors in model learning and inference for object detection and instance segmentation. IEEE Trans Cybern 52(8):8574\u2013858. https:\/\/doi.org\/10.1109\/TCYB.2021.3095305","journal-title":"IEEE Trans Cybern"},{"key":"6146_CR17","doi-asserted-by":"crossref","unstructured":"Geiger A, Lenz P, Urtasun R (2012) Are we ready for autonomous driving? the kitti vision benchmark suite. In: Conference on computer vision and pattern recognition (CVPR)","DOI":"10.1109\/CVPR.2012.6248074"},{"key":"6146_CR18","doi-asserted-by":"crossref","unstructured":"Cordts M, Omran M, Ramos S, et\u00a0al (2016) The cityscapes dataset for semantic urban scene understanding. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3213\u20133223","DOI":"10.1109\/CVPR.2016.350"},{"key":"6146_CR19","doi-asserted-by":"publisher","unstructured":"Tian Z, Shen C, Chen H, et\u00a0al (2019) Fcos: fully convolutional one-stage object detection. In: 2019 IEEE\/CVF international conference on computer vision (ICCV), pp 9626\u20139635. https:\/\/doi.org\/10.1109\/ICCV.2019.00972","DOI":"10.1109\/ICCV.2019.00972"},{"key":"6146_CR20","doi-asserted-by":"crossref","unstructured":"Zhang H, Wang Y, Dayoub F, et\u00a0al (2021) Varifocalnet: an iou-aware dense object detector. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 8514\u20138523","DOI":"10.1109\/CVPR46437.2021.00841"},{"key":"6146_CR21","unstructured":"Bochkovskiy A, Wang CY, Liao HYM (2020) Yolov4: optimal speed and accuracy of object detection. arXiv:2004.10934"},{"key":"6146_CR22","doi-asserted-by":"crossref","unstructured":"Dai X (2019) Hybridnet: a fast vehicle detection system for autonomous driving. Signal Process: Image Commun 70:79\u201388","DOI":"10.1016\/j.image.2018.09.002"},{"key":"6146_CR23","doi-asserted-by":"publisher","unstructured":"Jocher G (2020) Ultralytics yolov5. https:\/\/doi.org\/10.5281\/zenodo.3908559https:\/\/github.com\/ultralytics\/yolov5","DOI":"10.5281\/zenodo.3908559"},{"key":"6146_CR24","doi-asserted-by":"crossref","unstructured":"Hu X, Xu X, Xiao, et\u00a0al (2019) Sinet: a scale-insensitive convolutional neural network for fast vehicle detection. IEEE Trans Intell Transp Syst","DOI":"10.1109\/TITS.2018.2838132"},{"key":"6146_CR25","doi-asserted-by":"crossref","unstructured":"Xiang Y, Choi W, Lin Y, et\u00a0al (2017) Subcategory-aware convolutional neural networks for object proposals and detection. In: IEEE conference on computer vision and pattern recognition (CVPR)","DOI":"10.1109\/WACV.2017.108"},{"key":"6146_CR26","doi-asserted-by":"crossref","unstructured":"Mousavian A, Anguelov D, Flynn J, et\u00a0al (2017) 3d bounding box estimation using deep learning and geometry. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7074\u20137082","DOI":"10.1109\/CVPR.2017.597"},{"key":"6146_CR27","doi-asserted-by":"crossref","unstructured":"Chen X, Kundu K, Zhang Z, et\u00a0al (2016) Monocular 3d object detection for autonomous driving. In: IEEE conference on computer vision and pattern recognition (CVPR)","DOI":"10.1109\/CVPR.2016.236"},{"key":"6146_CR28","unstructured":"Ge Z, Liu S, Wang F, et\u00a0al (2021) Yolox: exceeding yolo series in 2021"},{"key":"6146_CR29","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 IEEE\/CVF conference on computer vision and pattern recognition, pp 7464\u20137475","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"6146_CR30","unstructured":"Jocher G, Chaurasia A, Qiu J (2023) Ultralytics yolov8. https:\/\/github.com\/ultralytics\/ultralytics"},{"key":"6146_CR31","doi-asserted-by":"crossref","unstructured":"Wang CY, Yeh IH, Liao HYM (2024a) Yolov9: learning what you want to learn using programmable gradient information. arXiv:2402.13616","DOI":"10.1007\/978-3-031-72751-1_1"},{"key":"6146_CR32","unstructured":"Wang A, Chen H, Liu L, et\u00a0al (2024b) Yolov10: real-time end-to-end object detection. arXiv:2405.14458"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-024-06146-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-024-06146-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-024-06146-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,6]],"date-time":"2025-09-06T03:58:47Z","timestamp":1757131127000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-024-06146-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,29]]},"references-count":32,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2025,4]]}},"alternative-id":["6146"],"URL":"https:\/\/doi.org\/10.1007\/s10489-024-06146-x","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,29]]},"assertion":[{"value":"3 December 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 January 2025","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest\/Competing interests"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"Not applicable.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Materials availability"}},{"value":"The code used in this study is available from the authors on request.","order":6,"name":"Ethics","group":{"name":"EthicsHeading","label":"Code availability"}}],"article-number":"389"}}