{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T12:53:49Z","timestamp":1744203229899,"version":"3.37.3"},"reference-count":31,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2021,11,30]],"date-time":"2021-11-30T00:00:00Z","timestamp":1638230400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,11,30]],"date-time":"2021-11-30T00:00:00Z","timestamp":1638230400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"name":"Natural\u00a0Science\u00a0Project\u00a0of\u00a0Shaanxi\u00a0Education\u00a0Department","award":["18JK0399"],"award-info":[{"award-number":["18JK0399"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["World Wide Web"],"published-print":{"date-parts":[[2022,7]]},"DOI":"10.1007\/s11280-021-00971-7","type":"journal-article","created":{"date-parts":[[2021,11,30]],"date-time":"2021-11-30T17:07:51Z","timestamp":1638292071000},"page":"1625-1648","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["DRA-ODM: a faster and more accurate deep recurrent attention dynamic model for object detection"],"prefix":"10.1007","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8876-2573","authenticated-orcid":false,"given":"Gaojie","family":"Li","sequence":"first","affiliation":[]},{"given":"Fei","family":"Xu","sequence":"additional","affiliation":[]},{"given":"He","family":"Li","sequence":"additional","affiliation":[]},{"given":"Yaoxuan","family":"Yuan","sequence":"additional","affiliation":[]},{"given":"Mingshou","family":"An","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,11,30]]},"reference":[{"issue":"04","key":"971_CR1","first-page":"1201","volume":"32","author":"C Keqi","year":"2021","unstructured":"Keqi, C., Zhiliang, Z., Xiaoming, D., Cuixia, Ma., Hongan, W.: A review of deep learning for multi-scale object detection[J]. Journal of Software 32(04), 1201\u20131227 (2021)","journal-title":"Journal of Software"},{"key":"971_CR2","doi-asserted-by":"crossref","unstructured":"LeCun Y, Bottou L, Bengio Y, et al. Gradient-based learning applied to document recognition[J]\/\/Proceedings of the IEEE, 1998, 86(11): 2278\u20132324.","DOI":"10.1109\/5.726791"},{"key":"971_CR3","doi-asserted-by":"crossref","unstructured":"Girshick R, Donahue J, Darrell T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]\/\/Proceedings of the IEEE conference on computer vision and pattern recognition. 2014: 580\u2013587.","DOI":"10.1109\/CVPR.2014.81"},{"key":"971_CR4","doi-asserted-by":"crossref","unstructured":"Girshick R. Fast r-cnn[C]\/\/Proceedings of the IEEE international conference on computer vision. 2015: 1440\u20131448.","DOI":"10.1109\/ICCV.2015.169"},{"key":"971_CR5","unstructured":"Ren S, He K, Girshick R, et al. Faster r-cnn: Towards real-time object detection with region proposal networks[J]\/\/Advances in neural information processing systems, 2015, 28: 91\u201399."},{"key":"971_CR6","doi-asserted-by":"crossref","unstructured":"Redmon J, Divvala S, Girshick R, et al. You only look once: Unified, real-time object detection[C]\/\/Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 779\u2013788.","DOI":"10.1109\/CVPR.2016.91"},{"key":"971_CR7","doi-asserted-by":"crossref","unstructured":"Redmon J, Farhadi A. YOLO9000: better, faster, stronger[C]\/\/Proceedings of the IEEE conference on computer vision and pattern recognition. 2017: 7263\u20137271.","DOI":"10.1109\/CVPR.2017.690"},{"key":"971_CR8","unstructured":"Redmon J, Farhadi A. Yolov3: An incremental improvement[J]. arXiv preprint arXiv:1804.02767, 2018."},{"key":"971_CR9","unstructured":"Bochkovskiy A, Wang C Y, Liao H Y M. Yolov4: Optimal speed and accuracy of object detection[J]. arXiv preprint arXiv:2004.10934, 2020."},{"key":"971_CR10","unstructured":"Zhou, Q, Wang, J, Liu, J. RSANet:\u00a0Towards\u00a0Real-Time\u00a0Object\u00a0Detection\u00a0with\u00a0Residual\u00a0Semantic-Guided\u00a0Attention\u00a0Feature\u00a0Pyramid\u00a0Network."},{"key":"971_CR11","doi-asserted-by":"crossref","unstructured":"Mobile\u00a0Netw\u00a0Appl\u00a026, 77\u201387\u00a0(2021).","DOI":"10.1007\/s11036-020-01723-z"},{"key":"971_CR12","doi-asserted-by":"crossref","unstructured":"Najibi M, Rastegari M, Davis L S. G-cnn: an iterative grid based object detector[C]\/\/Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 2369\u20132377.","DOI":"10.1109\/CVPR.2016.260"},{"issue":"8","key":"971_CR13","doi-asserted-by":"publisher","first-page":"7457","DOI":"10.1109\/JIOT.2020.2984887","volume":"7","author":"S Deng","year":"2020","unstructured":"Deng, S., Zhao, H., Fang, W., et al.: Edge intelligence: The confluence of edge computing and artificial intelligence[J]. IEEE Internet Things J. 7(8), 7457\u20137469 (2020)","journal-title":"IEEE Internet Things J."},{"issue":"3","key":"971_CR14","doi-asserted-by":"publisher","first-page":"194","DOI":"10.1038\/35058500","volume":"2","author":"L Itti","year":"2001","unstructured":"Itti, L., Koch, C.: Computational modelling of visual attention[J]. Nat. Rev. Neurosci. 2(3), 194\u2013203 (2001)","journal-title":"Nat. Rev. Neurosci."},{"key":"971_CR15","doi-asserted-by":"crossref","unstructured":"Mnih V, Kavukcuoglu K, Silver D, et al. Human-level control through deep reinforcement learning[J]. nature, 2015, 518(7540): 529\u2013533.","DOI":"10.1038\/nature14236"},{"key":"971_CR16","doi-asserted-by":"crossref","unstructured":"Mathe S, Pirinen A, Sminchisescu C. Reinforcement learning for visual object detection[C]\/\/Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 2894\u20132902.","DOI":"10.1109\/CVPR.2016.316"},{"key":"971_CR17","unstructured":"Sorokin I, Seleznev A, Pavlov M, et al. Deep attention recurrent Q-network[J]. arXiv preprint arXiv:1512.01693, 2015."},{"key":"971_CR18","unstructured":"Mnih V, Heess N, Graves A. Recurrent models of visual attention[C]\/\/Advances in neural information processing systems. 2014: 2204\u20132212."},{"key":"971_CR19","doi-asserted-by":"crossref","unstructured":"Hu J, Shen L, Sun G. Squeeze-and-excitation networks[C]\/\/Proceedings of the IEEE conference on computer vision and pattern recognition. 2018: 7132\u20137141.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"971_CR20","doi-asserted-by":"crossref","unstructured":"Woo S, Park J, Lee J Y, et al. Cbam: Convolutional block attention module[C]\/\/Proceedings of the European conference on computer vision (ECCV). 2018: 3\u201319.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"971_CR21","doi-asserted-by":"crossref","unstructured":"Li X, Wang W, Hu X, et al. Selective kernel networks[C]\/\/Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 2019: 510\u2013519.","DOI":"10.1109\/CVPR.2019.00060"},{"key":"971_CR22","unstructured":"Dosovitskiy A, Beyer L, Kolesnikov A, et al. An image is worth 16x16 words: Transformers for image recognition at scale[J]. arXiv preprint arXiv:2010.11929, 2020."},{"issue":"11","key":"971_CR23","doi-asserted-by":"publisher","first-page":"123","DOI":"10.3901\/JME.2019.11.123","volume":"55","author":"LV Jie","year":"2019","unstructured":"Jie, L.V.: Visual Attentional Network and Learning Method for Object Search and Recognition[J]. Journal of Mechanical Engineering 55(11), 123 (2019)","journal-title":"Journal of Mechanical Engineering"},{"key":"971_CR24","doi-asserted-by":"crossref","unstructured":"Shim D, Kim H J. Gaussian RAM: Lightweight Image Classification via Stochastic Retina-Inspired Glimpse and Reinforcement Learning[C]\/\/2020 20th International Conference on Control, Automation and Systems (ICCAS). IEEE, 2020: 155\u2013160.","DOI":"10.23919\/ICCAS50221.2020.9268201"},{"key":"971_CR25","doi-asserted-by":"crossref","unstructured":"Huang Y, Gu C, Wu K, et al. Parallel Search by Reinforcement Learning for Object Detection[C]\/\/Chinese Conference on Pattern Recognition and Computer Vision (PRCV). Springer, Cham, 2018: 272\u2013283.","DOI":"10.1007\/978-3-030-03341-5_23"},{"issue":"7","key":"971_CR26","first-page":"2544","volume":"31","author":"S Liu","year":"2019","unstructured":"Liu, S., Huang, D., Wang, Y.: Pay attention to them: deep reinforcement learning-based cascade object detection[J]. IEEE transactions on neural networks and learning systems 31(7), 2544\u20132556 (2019)","journal-title":"IEEE transactions on neural networks and learning systems"},{"key":"971_CR27","unstructured":"Krizhevsky A, Hinton G. Learning multiple layers of features from tiny images[J]. 2009."},{"issue":"2","key":"971_CR28","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","volume":"88","author":"M Everingham","year":"2010","unstructured":"Everingham, M., Van Gool, L., Williams, C.K.I., et al.: The pascal visual object classes (voc) challenge[J]. Int. J. Comput. Vision 88(2), 303\u2013338 (2010)","journal-title":"Int. J. Comput. Vision"},{"key":"971_CR29","doi-asserted-by":"crossref","unstructured":"Lu Y, Javidi T, Lazebnik S. Adaptive object detection using adjacency and zoom prediction[C]\/\/Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016: 2351\u20132359.","DOI":"10.1109\/CVPR.2016.258"},{"key":"971_CR30","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2017.2737479,5(4),pp.2315-2322","author":"H Lu","year":"2018","unstructured":"Lu, H., Li, Y., Mu, S., et al.: Motor Anomaly Detection for Unmanned Aerial Vehicles Using Reinforcement Learning[J]. IEEE Internet Things J. (2018). https:\/\/doi.org\/10.1109\/JIOT.2017.2737479,5(4),pp.2315-2322","journal-title":"IEEE Internet Things J."},{"key":"971_CR31","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2020.2991766","author":"H Lu","year":"2020","unstructured":"Lu, H., Zhang, Y., Li, Y., et al.: User-Oriented Virtual Mobile Network Resource Management for Vehicle Communications[J]. IEEE Trans. Intell. Transp. Syst. (2020). https:\/\/doi.org\/10.1109\/TITS.2020.2991766","journal-title":"IEEE Trans. Intell. Transp. Syst."}],"container-title":["World Wide Web"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11280-021-00971-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11280-021-00971-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11280-021-00971-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,26]],"date-time":"2022-07-26T08:15:24Z","timestamp":1658823324000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11280-021-00971-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,30]]},"references-count":31,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2022,7]]}},"alternative-id":["971"],"URL":"https:\/\/doi.org\/10.1007\/s11280-021-00971-7","relation":{},"ISSN":["1386-145X","1573-1413"],"issn-type":[{"type":"print","value":"1386-145X"},{"type":"electronic","value":"1573-1413"}],"subject":[],"published":{"date-parts":[[2021,11,30]]},"assertion":[{"value":"7 May 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 September 2021","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 October 2021","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 November 2021","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}