{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T18:45:07Z","timestamp":1747248307870,"version":"3.37.3"},"reference-count":38,"publisher":"Springer Science and Business Media LLC","issue":"23","license":[{"start":{"date-parts":[[2023,10,21]],"date-time":"2023-10-21T00:00:00Z","timestamp":1697846400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,10,21]],"date-time":"2023-10-21T00:00:00Z","timestamp":1697846400000},"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":["52171292, 51939001"],"award-info":[{"award-number":["52171292, 51939001"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the Outstanding Young Talent Program of Dalian","award":["2022RJ05"],"award-info":[{"award-number":["2022RJ05"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2023,12]]},"DOI":"10.1007\/s10489-023-05084-4","type":"journal-article","created":{"date-parts":[[2023,10,21]],"date-time":"2023-10-21T09:02:14Z","timestamp":1697878934000},"page":"29128-29139","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Multi-target vehicle detection based on corner pooling with attention mechanism"],"prefix":"10.1007","volume":"53","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4721-3671","authenticated-orcid":false,"given":"Li-Ying","family":"Hao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jia-Rui","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yunze","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jian","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,10,21]]},"reference":[{"issue":"9","key":"5084_CR1","doi-asserted-by":"publisher","first-page":"10430","DOI":"10.1007\/s10489-021-02798-1","volume":"52","author":"C Yan","year":"2022","unstructured":"Yan C, Zhang H, Li X, Yuan D (2022) R-SSD: Refined single shot multibox detector for pedestrian detection. Appl Intell 52(9):10430\u201310447","journal-title":"Appl Intell"},{"issue":"13","key":"5084_CR2","doi-asserted-by":"publisher","first-page":"9627","DOI":"10.1007\/s00521-023-08199-4","volume":"35","author":"LY Hao","year":"2023","unstructured":"Hao LY, Yang Z, Liu YP, Shen C (2023) TRCA-Net: stronger U structured network for human image segmentation. Neural Comput Appl 35(13):9627\u20139635","journal-title":"Neural Comput Appl"},{"issue":"5","key":"5084_CR3","first-page":"5532","volume":"53","author":"X Li","year":"2023","unstructured":"Li X, Kong D (2023) SRIF-RCNN: Sparsely represented inputs fusion of different sensors for 3D object detection. Appl Intell 53(5):5532\u20135553","journal-title":"Appl Intell"},{"issue":"3","key":"5084_CR4","doi-asserted-by":"publisher","first-page":"3183","DOI":"10.1007\/s10489-022-03412-8","volume":"53","author":"J Xiao","year":"2023","unstructured":"Xiao J, Yang L, Zhong F, Chen H, Li X (2023) Robust anomaly-based intrusion detection system for in-vehicle network by graph neural network framework. Appl Intell 53(3):3183\u20133206","journal-title":"Appl Intell"},{"issue":"5","key":"5084_CR5","doi-asserted-by":"publisher","first-page":"4481","DOI":"10.1109\/TGRS.2020.3012981","volume":"59","author":"B Zhao","year":"2021","unstructured":"Zhao B, Wang C, Fu Q, Han Z (2021) A novel pattern for infrared small target detection with generative adversarial network. IEEE Trans Geosci Remote Sens 59(5):4481\u20134492","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"5084_CR6","doi-asserted-by":"publisher","first-page":"3394","DOI":"10.1109\/JSTARS.2020.2998822","volume":"13","author":"D Pang","year":"2020","unstructured":"Pang D, Shan T, Li W, Ma P, Tao R (2020) Infrared dim and small target detection based on greedy bilateral factorization in image sequences. IEEE J Sel Top Appl Earth Obs Remote Sens 13:3394\u20133408","journal-title":"IEEE J Sel Top Appl Earth Obs Remote Sens"},{"key":"5084_CR7","doi-asserted-by":"crossref","unstructured":"Chadwick S, Maddern W, Newman P (2019) Distant vehicle detection using radar and vision, International Conference on Robotics and Automation (ICRA), Montreal, Canada, 8311-8317","DOI":"10.1109\/ICRA.2019.8794312"},{"issue":"1","key":"5084_CR8","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1109\/TITS.2019.2956813","volume":"22","author":"S Gilroy","year":"2019","unstructured":"Gilroy S, Jones E, Glavin M (2019) Overcoming occlusion in the automotive environment-A review. IEEE Trans Intell Transp Syst 22(1):23\u201335","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"5084_CR9","unstructured":"Redmon J, Farhadi A (2018) Yolov3: An incremental improvement. arXiv preprint arXiv: 1804.02767"},{"issue":"6","key":"5084_CR10","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","volume":"39","author":"S Ren","year":"2017","unstructured":"Ren S, He K, Girshick R, Sun J (2017) Faster R-CNN: Towards realtime 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":"5084_CR11","doi-asserted-by":"crossref","unstructured":"Girshick R, Donahue J, Darrell T, Malik J (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. IEEE Transactions on Computer Vision and Pattern Recognition(CVPR), Columbia, USA, 580-587","DOI":"10.1109\/CVPR.2014.81"},{"key":"5084_CR12","doi-asserted-by":"crossref","unstructured":"Girshick R (2015) Fast R-CNN. IEEE Transactions on International Conference on Computer Vision(ICCV), Santiago, Chile, 1440\u201348","DOI":"10.1109\/ICCV.2015.169"},{"key":"5084_CR13","doi-asserted-by":"crossref","unstructured":"Ren S, He K, Girshick R, Sun J (2017) Faster R-CNN: Towards realtime object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell 39(6):1137\u20131149","DOI":"10.1109\/TPAMI.2016.2577031"},{"key":"5084_CR14","doi-asserted-by":"crossref","unstructured":"Hei L, Jia D (2020) CornerNet: Detecting objects as paired keypoints. IEEE Transactions on European Conference on Computer Vision(ECCV), Glasgow, UK, 642\u2013656","DOI":"10.1007\/s11263-019-01204-1"},{"key":"5084_CR15","first-page":"6517","volume-title":"Yolo9000: Better, faster, stronger, IEEE Transactions on Computer Vision and Pattern Recognition(CVPR)","author":"J Redmon","year":"2017","unstructured":"Redmon J, Farhadi A (2017) Yolo9000: Better, faster, stronger, IEEE Transactions on Computer Vision and Pattern Recognition(CVPR). Honolulu, USA, pp 6517\u20136525"},{"key":"5084_CR16","doi-asserted-by":"crossref","unstructured":"Redmon J, Farhadi A (2017) Yolo9000: Better, faster, stronger, IEEE Transactions on Computer Vision and Pattern Recognition(CVPR). Honolulu, USA, pp 6517\u20136525","DOI":"10.1109\/CVPR.2017.690"},{"issue":"11","key":"5084_CR17","doi-asserted-by":"publisher","first-page":"4026","DOI":"10.3390\/s22114026","volume":"22","author":"J Oh","year":"2022","unstructured":"Oh J, Lee Y, Yoo J, Kwo S (2022) Improved Feature-Based Gaze Estimation Using Self-Attention Module and Synthetic Eye Images. Sensors 22(11):4026","journal-title":"Sensors"},{"key":"5084_CR18","doi-asserted-by":"publisher","first-page":"4045","DOI":"10.1109\/JSTARS.2022.3175191","volume":"15","author":"L Sun","year":"2022","unstructured":"Sun L, Cheng S, Zheng Y, Wu Z, Zhang J (2022) SPANet: Successive pooling attention network for semantic segmentation of remote sensing images. IEEE J Sel Top Appl Earth Obs Remote Sens 15:4045\u20134057","journal-title":"IEEE J Sel Top Appl Earth Obs Remote Sens"},{"key":"5084_CR19","first-page":"1","volume":"72","author":"C Liu","year":"2023","unstructured":"Liu C, Yi Z, Huang B, Zhou Z, Fang S, Li X, Zhang Y, Wu X (2023) A Deep Learning Method Based on Triplet Network Using Self-Attention for Tactile Grasp Outcomes Prediction. IEEE Trans Instrum Meas 72:1\u201314","journal-title":"IEEE Trans Instrum Meas"},{"issue":"6","key":"5084_CR20","doi-asserted-by":"publisher","DOI":"10.1007\/s11704-022-2126-1","volume":"17","author":"S Tan","year":"2023","unstructured":"Tan S, Zhang L, Shu X, Wang Z (2023) A feature-wise attention module based on the difference with surrounding features for convolutional neural networks. Front Comput Sci 17(6):176338","journal-title":"Front Comput Sci"},{"key":"5084_CR21","doi-asserted-by":"crossref","unstructured":"Tan S, Zhang L, Shu X, Wang Z (2023) A feature-wise attention module based on the difference with surrounding features for convolutional neural networks. Front Comput Sci 17(6):176338","DOI":"10.1007\/s11704-022-2126-1"},{"key":"5084_CR22","unstructured":"Newell A, Deng J (2017) Pixels to graphs by associative embedding. Adv Neural Inf Process 2172\u20132181"},{"issue":"1","key":"5084_CR23","first-page":"41","volume":"69","author":"Z Qin","year":"2022","unstructured":"Qin Z, Hanwen J, Qiyu D, Yuanhao Y, Long C, Qian W (2022) Robust Lane Detection From Continuous Driving Scenes Using Deep Neural Networks. IEEE Trans Veh Technol 69(1):41\u201354","journal-title":"IEEE Trans Veh Technol"},{"key":"5084_CR24","doi-asserted-by":"crossref","unstructured":"Tabelini L, Berriel R, Paixao, Thiago M, Badue C (2021) Keep your Eyes on the Lane: Real-time Attention-guided Lane Detection, 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition(CVPR), Nashvile, TN, USA, 294\u2013302","DOI":"10.1109\/CVPR46437.2021.00036"},{"key":"5084_CR25","doi-asserted-by":"crossref","unstructured":"Wang B, Wang G, Chan KL, Wang L (2014) Tracklet association with online target-specific metric learning, 2014 IEEE\/CVF Conference on Computer Vision and Pattern Recognition(CVPR), Columbus, USA, 1234\u20131241","DOI":"10.1109\/CVPR.2014.161"},{"issue":"1","key":"5084_CR26","first-page":"41","volume":"69","author":"Z Qin","year":"2022","unstructured":"Qin Z, Hanwen J, Qiyu D, Yuanhao Y, Long C, Qian W (2022) Robust Lane Detection From Continuous Driving Scenes Using Deep Neural Networks. IEEE Trans Veh Technol 69(1):41\u201354","journal-title":"IEEE Trans Veh Technol"},{"key":"5084_CR27","doi-asserted-by":"crossref","unstructured":"Hao LY, Li J, Guo G (2020) A multi-target corner pooling-based neural network for vehicle detection. Neural Comput Appl 32(18):14497\u201314506","DOI":"10.1007\/s00521-019-04486-1"},{"key":"5084_CR28","doi-asserted-by":"crossref","unstructured":"Yuan Z, Li X, Wang Q. Exploring Multi-Level Attention and Semantic Relationship for Remote Sensing Image Captioning. IEEE Access, 8, 2608-2620","DOI":"10.1109\/ACCESS.2019.2962195"},{"key":"5084_CR29","first-page":"2117","volume-title":"Microsoft coco: Common objects in context, 2014 Proceedings European Conference Computer Vision(ECCV)","author":"TY Lin","year":"2014","unstructured":"Lin TY, Maire M, Belongie S, Hays J, Perona P, Ramanan D, Zitnick CL (2014) Microsoft coco: Common objects in context, 2014 Proceedings European Conference Computer Vision(ECCV). Zurich, Switzerland, pp 2117\u20132125"},{"key":"5084_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2020.102907","volume":"193","author":"L Wen","year":"2020","unstructured":"Wen L, Du D, Cai Z et al (2020) UA-DETRAC: A new benchmark and protocol for multi-object detection and tracking. Comput Vis Image Underst 193:102907","journal-title":"Comput Vis Image Underst"},{"key":"5084_CR31","doi-asserted-by":"crossref","unstructured":"Zhu Y, Zhao C, Wang J, Xu Z, Lu H (2017) CoupleNet: Coupling global structure with local parts for object detection, 2017 IEEE Transactions on International Conference on Computer Vision(ICCV), Venice, Italy, 4146-4154","DOI":"10.1109\/ICCV.2017.444"},{"key":"5084_CR32","doi-asserted-by":"crossref","unstructured":"Dai J, Qi H, Xiong Y, Li Y, Zhang G, Hu H, Wei Y (2017) Deformable convolutional networks, 2017 IEEE Transactions on International Conference on Computer Vision(ICCV), Venice, Italy, 764\u2013773","DOI":"10.1109\/ICCV.2017.89"},{"issue":"18","key":"5084_CR33","doi-asserted-by":"publisher","first-page":"14497","DOI":"10.1007\/s00521-019-04486-1","volume":"32","author":"LY Hao","year":"2020","unstructured":"Hao LY, Li J, Guo G (2020) A multi-target corner pooling-based neural network for vehicle detection. Neural Comput Appl 32(18):14497\u201314506","journal-title":"Neural Comput Appl"},{"key":"5084_CR34","doi-asserted-by":"crossref","unstructured":"Shen Z, Liu Z, Li J, Jiang YG, Xue X (2017) DSOD: Learning deeply supervised object detectors from scratch, 2017 IEEE Transactions on International Conference on Computer Vision(ICCV), Venice, Italy, 1937\u20131945","DOI":"10.1109\/ICCV.2017.212"},{"key":"5084_CR35","unstructured":"Paszke A, Gross S, Chintala S, Chanan G, Yang E, Devito Z, Lin Z, Desmaison A, Antiga L, Lerer A (2017) Automatic differentiation in PyTorch, Workshop on Autodiff Decision Program, 1\u20134"},{"key":"5084_CR36","doi-asserted-by":"crossref","unstructured":"Wen L, Du D, Cai Z et\u00a0al (2020) UA-DETRAC: A new benchmark and protocol for multi-object detection and tracking. Comput Vis Image Underst 193:102907","DOI":"10.1016\/j.cviu.2020.102907"},{"key":"5084_CR37","doi-asserted-by":"crossref","unstructured":"Lin TY, Maire M, Belongie S, Hays J, Perona P, Ramanan D, Zitnick CL (2014) Microsoft coco: Common objects in context, 2014 Proceedings European Conference Computer Vision(ECCV). Zurich, Switzerland, pp 2117\u20132125","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"5084_CR38","unstructured":"King DP, Ba J (2014) Adam: A method for stochastic optimization. arXiv preprint arXiv: 1412.6980"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-023-05084-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-023-05084-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-023-05084-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,29]],"date-time":"2023-11-29T14:27:00Z","timestamp":1701268020000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-023-05084-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,21]]},"references-count":38,"journal-issue":{"issue":"23","published-print":{"date-parts":[[2023,12]]}},"alternative-id":["5084"],"URL":"https:\/\/doi.org\/10.1007\/s10489-023-05084-4","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"type":"print","value":"0924-669X"},{"type":"electronic","value":"1573-7497"}],"subject":[],"published":{"date-parts":[[2023,10,21]]},"assertion":[{"value":"30 September 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 October 2023","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 declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}}]}}