{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T15:39:19Z","timestamp":1774453159057,"version":"3.50.1"},"reference-count":31,"publisher":"Institute of Electronics, Information and Communications Engineers (IEICE)","issue":"5","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEICE Trans. Fundamentals"],"published-print":{"date-parts":[[2020,5,1]]},"DOI":"10.1587\/transfun.2019eap1135","type":"journal-article","created":{"date-parts":[[2020,4,30]],"date-time":"2020-04-30T22:13:32Z","timestamp":1588284812000},"page":"769-779","source":"Crossref","is-referenced-by-count":4,"title":["Vehicle Key Information Detection Algorithm Based on Improved SSD"],"prefix":"10.1587","volume":"E103.A","author":[{"given":"Ende","family":"WANG","sequence":"first","affiliation":[{"name":"Key Laboratory of Optical Electrical Image Processing, Shenyang Institute of Automation, Chinese Academy of Sciences"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yong","family":"LI","sequence":"additional","affiliation":[{"name":"Key Laboratory of Optical Electrical Image Processing, Shenyang Institute of Automation, Chinese Academy of Sciences"},{"name":"College of Information Science and Engineering, Northeastern University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuebin","family":"WANG","sequence":"additional","affiliation":[{"name":"School of Land Science and Technology, China University of Geosciences"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peng","family":"WANG","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Northeastern University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinlei","family":"JIAO","sequence":"additional","affiliation":[{"name":"Key Laboratory of Optical Electrical Image Processing, Shenyang Institute of Automation, Chinese Academy of Sciences"},{"name":"College of Information Science and Engineering, Northeastern University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaosheng","family":"YU","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Northeastern University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"532","reference":[{"key":"1","unstructured":"[1] H. Jia and Y. Zhang, \u201cA review of pedestrian detection based on computer vision in vehicle-assisted driving systems,\u201d ACTA Automatica Sinical, vol.01, pp.84-90, 2007."},{"key":"2","unstructured":"[2] Q. Li, X. Chen, and L. Wang, \u201cDetection and recognition of moving objects in video monitoring,\u201d Computer Engineering, vol.30, no.16, pp.143-145, 2004."},{"key":"3","unstructured":"[3] S. Ren, K. He, R.B. Girshick, and J. Sun, \u201cFaster R-CNN: Towards real-time object detection with region proposal networks,\u201d Adv. in NIPS, pp.91-99, 2015."},{"key":"4","doi-asserted-by":"crossref","unstructured":"[4] W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A.C. Berg, \u201cSSD: Single shot MultiBox detector,\u201d Proc. the ECCV, pp.21-37, 2016. 10.1007\/978-3-319-46448-0_2","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"5","doi-asserted-by":"publisher","unstructured":"[5] M. Everingham, L. VanGool, C.K.I. Williams, J. Winn, and A. Zisserman, \u201cThe PASCAL visual object classes (VOC) challenge,\u201d Int. J. Comput. Vision., vol.88, no.2, pp.303-338, 2010. 10.1007\/s11263-009-0275-4","DOI":"10.1007\/s11263-009-0275-4"},{"key":"6","doi-asserted-by":"crossref","unstructured":"[6] A. Agrawal, P. Mangalraj, and M.A. Bisherwal, \u201cTarget detection in SAR images using SIFT,\u201d IEEE International Symposium on Signal Processing and Information Technology, IEEE, pp.90-94, 2016. 10.1109\/isspit.2015.7394426","DOI":"10.1109\/ISSPIT.2015.7394426"},{"key":"7","doi-asserted-by":"crossref","unstructured":"[7] X. Fan, Y. Cheng, and Q. Fu, \u201cMoving target detection algorithm based on Susan edge detection and frame difference,\u201d International Conference on Information Science and Control Engineering, IEEE, pp.323-326, 2015. 10.1109\/icisce.2015.78","DOI":"10.1109\/ICISCE.2015.78"},{"key":"8","unstructured":"[8] K. Meng, G. Minggang, and C. Tao, \u201cThe corner matching based on improved singular value decomposition for motion detection,\u201d IEEE Control Conference, pp.3727-3732, 2012."},{"key":"9","doi-asserted-by":"crossref","unstructured":"[9] K.L. Lee and M.M. Mokji, \u201cAutomatic target detection in GPR images using histogram of oriented gradients (HOG),\u201d International Conference on Electronic Design, IEEE, pp.181-186, 2015. 10.1109\/iced.2014.7015795","DOI":"10.1109\/ICED.2014.7015795"},{"key":"10","doi-asserted-by":"crossref","unstructured":"[10] P. Singh, B.B.V.L. Deepak, T. Sethi, and M.D.P. Murthy, \u201cReal-time object detection and tracking using color feature and motion,\u201d International Conference on Communications and Signal Processing, IEEE, pp.1236-1241, 2015. 10.1109\/iccsp.2015.7322705","DOI":"10.1109\/ICCSP.2015.7322705"},{"key":"11","unstructured":"[11] Y.P. Cui, S. Zheng, Y.C. Liu, \u201cSVM-based infrared small target detection,\u201d Infrared &amp; Laser Engineering, vol.34, no.6, pp.696-702, 2005."},{"key":"12","unstructured":"[12] S. Sun, Z. Xu, X. Wang, G. Huang, W. Wu, and D. Xu, \u201cReal-time vehicle detection using Haar-SURF mixed features and gentle AdaBoost classifier,\u201d Control and Decision Conference, IEEE, pp.1888-1894, 2015. 10.1109\/ccdc.2015.7162227"},{"key":"13","unstructured":"[13] Q.-L. Li and J.-F. He, \u201cVehicles detection based on three-frame-difference method and cross-entropy threshold method,\u201d Computer Engineering, vol.37, no.4, pp.172-174, 2011."},{"key":"14","doi-asserted-by":"crossref","unstructured":"[14] P. Kaew Tra Kul Pong and R. Bowden, \u201cAn improved adaptive background mixture model for real-time tracking with shadow detection.Video-based surveillance systems,\u201d Video-Based Surveillance Systems, pp.135-144, Springer US, 2002. 10.1007\/978-1-4615-0913-4_11","DOI":"10.1007\/978-1-4615-0913-4_11"},{"key":"15","doi-asserted-by":"crossref","unstructured":"[15] A. Ilyas, M. Scuturici, and S. Miguet, \u201cReal time foreground-background segmentation using a modified codebook model,\u201d 2009 Advanced Video and Signal Based Surveillance, pp.454-459, 2009. 10.1109\/avss.2009.85","DOI":"10.1109\/AVSS.2009.85"},{"key":"16","doi-asserted-by":"publisher","unstructured":"[16] A. Fern\u00e1ndez-Caballero, J. Mira, M.A. Fern\u00e1ndez, and A.E. Delgado, \u201cOn motion detection through a multi-layer neural network architecture,\u201d Neural Networks, vol.16, no.2, pp.205-222, 2003. 10.1016\/s0893-6080(02)00233-2","DOI":"10.1016\/S0893-6080(02)00233-2"},{"key":"17","unstructured":"[17] Y. Sun, D. Liang, X. Wang, et al., \u201cDeepID3: Face recognition with very deep neural networks,\u201d Computer Science, 2015."},{"key":"18","doi-asserted-by":"publisher","unstructured":"[18] R. Girshick, J. Donahue, T. Darrell, and J. Malik, \u201cRegion-based convolutional networks for accurate object detection and segmentation,\u201d IEEE Trans. Pattern Anal. Mach. Intell., vol.38, no.1, pp.142-158, 2016. 10.1109\/tpami.2015.2437384","DOI":"10.1109\/TPAMI.2015.2437384"},{"key":"19","doi-asserted-by":"crossref","unstructured":"[19] R. Girshick and R. Girshick, \u201cFast R-CNN,\u201d Computer Science, 2015.","DOI":"10.1109\/ICCV.2015.169"},{"key":"20","doi-asserted-by":"crossref","unstructured":"[20] C. Eggert, S. Brehm, A. Winschel, D. Zecha, and R. Lienhart, \u201cA closer look: Small object detection in faster R-CNN,\u201d IEEE International Conference on Multimedia and Expo. IEEE Computer Society, pp.421-426, 2017. 10.1109\/icme.2017.8019550","DOI":"10.1109\/ICME.2017.8019550"},{"key":"21","doi-asserted-by":"crossref","unstructured":"[21] J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, \u201cYou only look once: Unified, real-time object detection,\u201d Proc. IEEE Conference on Computer Vision and Pattern Recognition, pp.779-788, 2016. 10.1109\/cvpr.2016.91","DOI":"10.1109\/CVPR.2016.91"},{"key":"22","unstructured":"[22] Q. Zhou, \u201cMulti-target real-time detection of moving ship based on YOLO algorithm,\u201d Computer Knowledge &amp; Technology, 2018."},{"key":"23","doi-asserted-by":"crossref","unstructured":"[23] L. Wang, Y. Lu, H. Wang, Y. Zheng, H. Ye, and X. Xue, \u201cEvolving boxes for fast vehicle detection,\u201d IEEE International Conference on Multimedia and Expo, IEEE, pp.1135-1140, 2017. 10.1109\/icme.2017.8019461","DOI":"10.1109\/ICME.2017.8019461"},{"key":"24","unstructured":"[24] K. Simonyan and A. Zisserman, \u201cVery deep convolutional networks for large-scale image recognition,\u201d Computer Science, 2014."},{"key":"25","doi-asserted-by":"crossref","unstructured":"[25] J. Li, C. Niu, and M. Fan, \u201cMulti-scale convolutional neural networks for natural scene license plate detection,\u201d International Symposium on Neural Networks, pp.110-119, 2012. 10.1007\/978-3-642-31362-2_13","DOI":"10.1007\/978-3-642-31362-2_13"},{"key":"26","doi-asserted-by":"crossref","unstructured":"[26] M.D. Zeiler, D. Krishnan, G.W. Taylor, and R. Fergus, \u201cDeconvolutional networks,\u201d Computer Vision and Pattern Recognition, IEEE, pp.2528-2535, 2010. 10.1109\/cvpr.2010.5539957","DOI":"10.1109\/CVPR.2010.5539957"},{"key":"27","doi-asserted-by":"crossref","unstructured":"[27] G. Krell, R. Niese, B. Michaelis, \u201cFacial expression recognition with multi-channel deconvolution,\u201d International Conference on Advances in Pattern Recognition, IEEE, pp.413-416, 2009. 10.1109\/icapr.2009.95","DOI":"10.1109\/ICAPR.2009.95"},{"key":"28","doi-asserted-by":"crossref","unstructured":"[28] P.O. Pinheiro, T.Y. Lin, R. Collobert, and P. Doll\u00e1r, \u201cLearning to refine object segments,\u201d European Conference on Computer Vision, pp.75-91, Springer, Cham, 2016. 10.1007\/978-3-319-46448-0_5","DOI":"10.1007\/978-3-319-46448-0_5"},{"key":"29","doi-asserted-by":"crossref","unstructured":"[29] Z. Cai, Q. Fan, R. Feris, and N. Vasconcelos, \u201cA unified multi-scale deep convolutional neural network for fast object detection,\u201d 14th European Conference on Computer Vision (ECCV), Amsterdam, Netherlands, Springer, 2016. 10.1007\/978-3-319-46493-0_22","DOI":"10.1007\/978-3-319-46493-0_22"},{"key":"30","doi-asserted-by":"publisher","unstructured":"[30] Z. Tu, W. Xie, Q. Qin, R. Poppe, R.C. Veltkamp, B. Li, and J. Yuan, \u201cMulti-stream CNN: Learning representations based on human-related regions for action recognition,\u201d Pattern Recognition, vol.79, pp.32-43, 2018. 10.1016\/j.patcog.2018.01.020","DOI":"10.1016\/j.patcog.2018.01.020"},{"key":"31","doi-asserted-by":"crossref","unstructured":"[31] J. Redmonand and A. Farhadi, \u201cYOLO9000: Better, faster, stronger,\u201d Computer Vision and Pattern Recognition (CVPR), 2017 IEEE Conference on, pp.6517-6525, IEEE, 2017. 10.1109\/cvpr.2017.690","DOI":"10.1109\/CVPR.2017.690"}],"container-title":["IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transfun\/E103.A\/5\/E103.A_2019EAP1135\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,5,7]],"date-time":"2020-05-07T06:44:11Z","timestamp":1588833851000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transfun\/E103.A\/5\/E103.A_2019EAP1135\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,5,1]]},"references-count":31,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2020]]}},"URL":"https:\/\/doi.org\/10.1587\/transfun.2019eap1135","relation":{},"ISSN":["0916-8508","1745-1337"],"issn-type":[{"value":"0916-8508","type":"print"},{"value":"1745-1337","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,5,1]]}}}