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Secondly, the frameworks of object detection (e.g. Region-based CNN (R-CNN), Spatial Pyramid Pooling Network (SPP-NET), Fast-RCNN and Faster-RCNN) which combine region proposals and convolutional neural networks (CNNs) are briefly characterized for optical remote sensing applications. You only look once (YOLO) algorithm is the representative of the object detection frameworks (e.g. YOLO and Single Shot MultiBox Detector (SSD)) which transforms the object detection into a regression problem. The limitations of remote sensing images and object detectors have been highlighted and discussed. The feasibility and limitations of these approaches will lead researchers to prudently select appropriate image enhancements. Finally, the problems of object detection algorithms in deep learning are summarized and the future recommendations are also conferred.<\/jats:p>","DOI":"10.3233\/mgs-200330","type":"journal-article","created":{"date-parts":[[2020,11,6]],"date-time":"2020-11-06T11:51:00Z","timestamp":1604663460000},"page":"227-243","source":"Crossref","is-referenced-by-count":33,"title":["A brief review and challenges of object detection in optical remote sensing imagery"],"prefix":"10.1177","volume":"16","author":[{"given":"Shahid","family":"Karim","sequence":"first","affiliation":[{"name":"Department of Computer Science, ILMA University, Karachi, Pakistan"},{"name":"School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ye","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shoulin","family":"Yin","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Irfana","family":"Bibi","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Xidian University, Xi\u2019an, Shaanxi 710071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ali Anwar","family":"Brohi","sequence":"additional","affiliation":[{"name":"School of Energy Science and Engineering, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","reference":[{"key":"10.3233\/MGS-200330_ref1","doi-asserted-by":"crossref","unstructured":"J. Gall and V. Lempitsky, Class-specific hough forests for object detection, in: Decision Forests for Computer Vision and Medical Image Analysis, Springer, 2013, pp. 143\u2013157.","DOI":"10.1007\/978-1-4471-4929-3_11"},{"issue":"8","key":"10.3233\/MGS-200330_ref2","doi-asserted-by":"crossref","first-page":"1532","DOI":"10.1109\/TPAMI.2014.2300479","article-title":"Fast feature pyramids for object detection","volume":"36","author":"Doll\u00e1r","year":"2014","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"6","key":"10.3233\/MGS-200330_ref3","doi-asserted-by":"crossref","first-page":"1309","DOI":"10.3390\/s19061309","article-title":"Multi-vehicle tracking via real-time detection probes and a markov decision process policy","volume":"19","author":"Zou","year":"2019","journal-title":"Sensors"},{"key":"10.3233\/MGS-200330_ref4","doi-asserted-by":"crossref","unstructured":"K.A. Brookhuis, D. De Waard and W.H. Janssen, Behavioural impacts of advanced driver assistance systems \u2013 an overview, Eur. J. Transp. Infrastruct. Res. 1(3) (2019).","DOI":"10.18757\/EJTIR.2001.1.3.3667"},{"key":"10.3233\/MGS-200330_ref5","doi-asserted-by":"crossref","first-page":"4538","DOI":"10.1109\/TITS.2018.2888500","article-title":"On-road vehicle tracking using part-based particle filter","volume":"12","author":"Fang","year":"2019","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"10.3233\/MGS-200330_ref6","doi-asserted-by":"crossref","unstructured":"C. Caraffi, T. Voj\u00ed\u0159, J. Trefn\u00fd, J. \u0160ochman and J. Matas, A system for real-time detection and tracking of vehicles from a single car-mounted camera, in: 2012 15th International IEEE Conference on Intelligent Transportation Systems, 2012, pp.\u00a0975\u2013982.","DOI":"10.1109\/ITSC.2012.6338748"},{"issue":"2","key":"10.3233\/MGS-200330_ref7","doi-asserted-by":"crossref","first-page":"525","DOI":"10.1109\/TITS.2015.2479925","article-title":"Multiple sensor fusion and classification for moving object detection and tracking","volume":"17","author":"Chavez-Garcia","year":"2016","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"10.3233\/MGS-200330_ref8","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1016\/j.jvcir.2015.03.003","article-title":"Moving object detection and tracking from video captured by moving camera","volume":"30","author":"Hu","year":"2015","journal-title":"J. Vis. Commun. Image Represent."},{"key":"10.3233\/MGS-200330_ref9","doi-asserted-by":"crossref","unstructured":"J.S. Kulchandani and K. J.Dangarwala, Moving object detection: Review of recent research trends, in: 2015 International Conference on Pervasive Computing (ICPC), 2015, pp. 1\u20135.","DOI":"10.1109\/PERVASIVE.2015.7087138"},{"issue":"2","key":"10.3233\/MGS-200330_ref10","doi-asserted-by":"crossref","first-page":"450","DOI":"10.1109\/TPAMI.2012.97","article-title":"Simultaneous video stabilization and moving object detection in turbulence","volume":"35","author":"Oreifej","year":"2013","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"3","key":"10.3233\/MGS-200330_ref11","doi-asserted-by":"crossref","first-page":"597","DOI":"10.1109\/TPAMI.2012.132","article-title":"Moving object detection by detecting contiguous outliers in the low-rank representation","volume":"35","author":"Zhou","year":"2013","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.3233\/MGS-200330_ref12","doi-asserted-by":"crossref","unstructured":"H. Jiang, J. Wang, Z. Yuan, Y. Wu, N. Zheng and S. Li, Salient object detection: A discriminative regional feature integration approach, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (Portland, Oregon), 2013, pp. 2083\u20132090.","DOI":"10.1109\/CVPR.2013.271"},{"key":"10.3233\/MGS-200330_ref13","unstructured":"C. Szegedy, A. Toshev and D. Erhan, Deep neural networks for object detection, in: Advances in Neural Information Processing Systems, 2013, pp. 2553\u20132561."},{"key":"10.3233\/MGS-200330_ref14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2019\/8597606","article-title":"DMCNN: A deep multiscale convolutional neural network model for medical image segmentation","volume":"2019","author":"Teng","year":"2019","journal-title":"J. Healthc. Eng."},{"key":"10.3233\/MGS-200330_ref15","doi-asserted-by":"publisher","first-page":"26069","DOI":"10.1109\/ACCESS.2018.2834960","article-title":"Large scale remote sensing image segmentation based on fuzzy region competition and gaussian mixture model","volume":"6","author":"Yin","year":"2018","journal-title":"IEEE Access"},{"issue":"5","key":"10.3233\/MGS-200330_ref16","article-title":"Region search based on hybrid convolutional neural network in optical remote sensing images","volume":"15","author":"Yin","year":"2019","journal-title":"Int. J. Distrib. Sens. Networks"},{"key":"10.3233\/MGS-200330_ref17","first-page":"1","article-title":"An optimised multi-scale fusion method for airport detection in large-scale optical remote sensing images","author":"Yin","year":"2020","journal-title":"Int. J. Image Data Fusion"},{"key":"10.3233\/MGS-200330_ref18","doi-asserted-by":"crossref","unstructured":"X. Wang, T.X. Han and S. Yan, An HOG-LBP human detector with partial occlusion handling, in: Computer Vision, 2009 IEEE 12th International Conference on, 2009, pp. 32\u201339.","DOI":"10.1109\/ICCV.2009.5459207"},{"key":"10.3233\/MGS-200330_ref19","doi-asserted-by":"crossref","unstructured":"N. Dalal and B. Triggs, Histograms of oriented gradients for human detection, in: Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, 1, 2005, pp. 886\u2013893.","DOI":"10.1109\/CVPR.2005.177"},{"key":"10.3233\/MGS-200330_ref20","doi-asserted-by":"crossref","unstructured":"Q. Zhu, M.-C. Yeh, K.-T. Cheng and S. Avidan, Fast human detection using a cascade of histograms of oriented gradients, in: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201906), 2, 2006, pp.\u00a01491\u20131498.","DOI":"10.1109\/CVPR.2006.119"},{"issue":"2","key":"10.3233\/MGS-200330_ref21","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","article-title":"Distinctive image features from scale-invariant keypoints","volume":"60","author":"Lowe","year":"2004","journal-title":"Int. J. Comput. Vis."},{"issue":"4","key":"10.3233\/MGS-200330_ref22","first-page":"143","article-title":"A comparison of sift, pca-sift and surf","volume":"3","author":"Juan","year":"2009","journal-title":"Int. J. Image Process."},{"issue":"5","key":"10.3233\/MGS-200330_ref23","doi-asserted-by":"crossref","first-page":"978","DOI":"10.1109\/TPAMI.2010.147","article-title":"Sift flow: Dense correspondence across scenes and its applications","volume":"33","author":"Liu","year":"2011","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.3233\/MGS-200330_ref24","doi-asserted-by":"crossref","unstructured":"S.K. Divvala, A.A. Efros and M. Hebert, How important are \u2018deformable parts\u2019 in the deformable parts model, in: European Conference on Computer Vision, 2012, pp. 31\u201340.","DOI":"10.1007\/978-3-642-33885-4_4"},{"issue":"9","key":"10.3233\/MGS-200330_ref25","doi-asserted-by":"crossref","first-page":"1627","DOI":"10.1109\/TPAMI.2009.167","article-title":"Object detection with discriminatively trained part-based models","volume":"32","author":"Felzenszwalb","year":"2010","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.3233\/MGS-200330_ref26","doi-asserted-by":"crossref","unstructured":"R. Girshick, F. Iandola, T. Darrell and J. Malik, Deformable part models are convolutional neural networks, in: Proceedings of the IEEE conference on Computer Vision and Pattern Recognition (Boston, Massachusetts), 2015, pp. 437\u2013446.","DOI":"10.1109\/CVPR.2015.7298641"},{"key":"10.3233\/MGS-200330_ref27","doi-asserted-by":"crossref","unstructured":"W. Ouyang and X. Wang, Joint deep learning for pedestrian detection, in: Proceedings of the IEEE International Conference on Computer Vision (Sydney, NSW, Australia), 2013, pp. 2056\u20132063.","DOI":"10.1109\/ICCV.2013.257"},{"issue":"2","key":"10.3233\/MGS-200330_ref28","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1007\/s11263-013-0620-5","article-title":"Selective search for object recognition","volume":"104","author":"Uijlings","year":"2013","journal-title":"Int. J. Comput. Vis."},{"issue":"3","key":"10.3233\/MGS-200330_ref30","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","article-title":"Imagenet large scale visual recognition challenge","volume":"115","author":"Russakovsky","year":"2015","journal-title":"Int. J. Comput. Vis."},{"key":"10.3233\/MGS-200330_ref31","doi-asserted-by":"crossref","unstructured":"T.-Y. Lin et al., Microsoft coco: Common objects in context, in: European Conference on Computer Vision, 2014, pp.\u00a0740\u2013755.","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"10.3233\/MGS-200330_ref32","unstructured":"A. Krizhevsky, I. Sutskever and G.E. Hinton, Imagenet classification with deep convolutional neural networks, in: Advances in Neural Information Processing Systems, 2012, pp. 1097\u20131105."},{"key":"10.3233\/MGS-200330_ref33","doi-asserted-by":"crossref","unstructured":"J. Long, E. Shelhamer and T. Darrell, Fully convolutional networks for semantic segmentation, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (Boston, Massachusetts), 2015, pp. 3431\u20133440.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"10.3233\/MGS-200330_ref34","doi-asserted-by":"crossref","first-page":"993","DOI":"10.1109\/34.58871","article-title":"Neural network ensembles","volume":"10","author":"Hansen","year":"1990","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.3233\/MGS-200330_ref35","doi-asserted-by":"crossref","unstructured":"B. Kingsbury, T.N. Sainath and H. Soltau, Scalable minimum Bayes risk training of deep neural network acoustic models using distributed Hessian-free optimization, in: Thirteenth Annual Conference of the International Speech Communication Association, 2012.","DOI":"10.21437\/Interspeech.2012-3"},{"key":"10.3233\/MGS-200330_ref36","unstructured":"L. Xu, J.S.J. Ren, C. Liu and J. Jia, Deep convolutional neural network for image deconvolution, in: Advances in Neural Information Processing Systems, 2014, pp. 1790\u20131798."},{"key":"10.3233\/MGS-200330_ref38","doi-asserted-by":"crossref","unstructured":"C. Szegedy et al., Going deeper with convolutions, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (Boston, Massachusetts), 2015, pp. 1\u20139.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"10.3233\/MGS-200330_ref39","doi-asserted-by":"crossref","unstructured":"K. He, X. Zhang, S. Ren and J. Sun, Deep residual learning for image recognition, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (Las Vegas, NV, USA), 2016, pp. 770\u2013778.","DOI":"10.1109\/CVPR.2016.90"},{"key":"10.3233\/MGS-200330_ref40","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.81"},{"key":"10.3233\/MGS-200330_ref41","doi-asserted-by":"crossref","unstructured":"K. He, X. Zhang, S. Ren and J. Sun, Spatial pyramid pooling in deep convolutional networks for visual recognition, in: European Conference on Computer Vision, 2014, pp. 346\u2013361.","DOI":"10.1007\/978-3-319-10578-9_23"},{"key":"10.3233\/MGS-200330_ref42","doi-asserted-by":"crossref","unstructured":"R. Girshick, Fast r-cnn, in: Proceedings of the IEEE International Conference on Computer Vision (Boston, Massachusetts), 2015, pp. 1440\u20131448.","DOI":"10.1109\/ICCV.2015.169"},{"key":"10.3233\/MGS-200330_ref43","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: Towards real-time object detection with region proposal networks","author":"Ren","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.3233\/MGS-200330_ref44","unstructured":"J. Dai, Y. Li, K. He and J. Sun, R-fcn: Object detection via region-based fully convolutional networks, in: Advances in Neural Information Processing Systems, 2016, pp. 379\u2013387."},{"key":"10.3233\/MGS-200330_ref45","doi-asserted-by":"crossref","unstructured":"J. Redmon, S. Divvala, R. Girshick and A. Farhadi, You only look once: Unified, real-time object detection, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (Las Vegas, NV, USA), 2016, pp. 779\u2013788.","DOI":"10.1109\/CVPR.2016.91"},{"key":"10.3233\/MGS-200330_ref46","doi-asserted-by":"crossref","unstructured":"W. Liu et al., Ssd: Single shot multibox detector, in: European Conference on Computer Vision, 2016, pp. 21\u201337.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"10.3233\/MGS-200330_ref47","doi-asserted-by":"crossref","unstructured":"J. Zhang, Y. Huang and K. Huang, Data decomposition and spatial mixture modeling for part based model, in: Asian Conference on Computer Vision, 2012, pp. 123\u2013137.","DOI":"10.1007\/978-3-642-37331-2_10"},{"key":"10.3233\/MGS-200330_ref48","doi-asserted-by":"crossref","unstructured":"P.F. Felzenszwalb, R.B. Girshick and D. McAllester, Cascade object detection with deformable part models, in: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010, pp. 2241\u20132248.","DOI":"10.1109\/CVPR.2010.5539906"},{"key":"10.3233\/MGS-200330_ref49","doi-asserted-by":"crossref","unstructured":"N. Zhang, J. Donahue and R. Girshick, Part-based R-CNNs for fine-grained category detection, in: European Conference on Computer Vision, 2014, pp. 834\u2013849.","DOI":"10.1007\/978-3-319-10590-1_54"},{"key":"10.3233\/MGS-200330_ref50","doi-asserted-by":"crossref","unstructured":"A. Shrivastava, A. Gupta and R. Girshick, Training region-based object detectors with online hard example mining, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (Las Vegas, NV, USA), 2016, pp.\u00a0761\u2013769.","DOI":"10.1109\/CVPR.2016.89"},{"key":"10.3233\/MGS-200330_ref51","doi-asserted-by":"crossref","unstructured":"T. Kong, A. Yao, Y. Chen and F. Sun, Hypernet: Towards accurate region proposal generation and joint object detection, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (Las Vegas, NV, USA), 2016, pp.\u00a0845\u2013853.","DOI":"10.1109\/CVPR.2016.98"},{"issue":"2","key":"10.3233\/MGS-200330_ref52","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1023\/A:1023052124951","article-title":"Contextual priming for object detection","volume":"53","author":"Torralba","year":"2003","journal-title":"Int. J. Comput. Vis."},{"issue":"3\u20134","key":"10.3233\/MGS-200330_ref53","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1016\/j.isprsjprs.2003.10.002","article-title":"Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information","volume":"58","author":"Benz","year":"2004","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"10.3233\/MGS-200330_ref54","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2016.01.011","article-title":"Random forest in remote sensing: A review of applications and future directions","volume":"114","author":"Belgiu","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"10.3233\/MGS-200330_ref55","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.isprsjprs.2016.03.014","article-title":"A survey on object detection in optical remote sensing images","volume":"117","author":"Cheng","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"10.3233\/MGS-200330_ref56","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.isprsjprs.2013.03.006","article-title":"Change detection from remotely sensed images: From pixel-based to object-based approaches","volume":"80","author":"Hussain","year":"2013","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"10.3233\/MGS-200330_ref57","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.neucom.2018.11.081","article-title":"Single infrared image enhancement using a deep convolutional neural network","volume":"332","author":"Kuang","year":"2019","journal-title":"Neurocomputing"},{"issue":"12","key":"10.3233\/MGS-200330_ref58","doi-asserted-by":"crossref","first-page":"1582","DOI":"10.1109\/TPAMI.2003.1251151","article-title":"Neural edge enhancer for supervised edge enhancement from noisy images","volume":"25","author":"Suzuki","year":"2003","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.3233\/MGS-200330_ref60","doi-asserted-by":"crossref","unstructured":"Y. Piao and H. Park, Image resolution enhancement using inter-subband correlation in wavelet domain, in: 2007 IEEE International Conference on Image Processing, 1, 2007, pp. I\u2013445.","DOI":"10.1109\/ICIP.2007.4378987"},{"key":"10.3233\/MGS-200330_ref61","unstructured":"S. Han, J. Pool, J. Tran and W. Dally, Learning both weights and connections for efficient neural network, in: Advances in Neural Information Processing Systems, 2015, pp. 1135\u20131143."},{"key":"10.3233\/MGS-200330_ref63","doi-asserted-by":"crossref","unstructured":"X. Chen, S. Xiang, C.-L. Liu and C.-H. Pan, Vehicle detection in satellite images by parallel deep convolutional neural networks, in: Pattern Recognition (ACPR), 2013 2nd IAPR Asian Conference on, 2013, pp. 181\u2013185.","DOI":"10.1109\/ACPR.2013.33"},{"key":"10.3233\/MGS-200330_ref64","doi-asserted-by":"crossref","unstructured":"J. Hu, T. Xu, J. Zhang and Y. Yang, Fast Vehicle Detection in Satellite Images Using Fully Convolutional Network, in: Chinese Conference on Intelligent Visual Surveillance, 2016, pp. 122\u2013129.","DOI":"10.1007\/978-981-10-3476-3_15"},{"issue":"2","key":"10.3233\/MGS-200330_ref65","first-page":"158","article-title":"Vehicle detection in satellite images by incorporating objectness and convolutional neural network","volume":"4","author":"Qu","year":"2016","journal-title":"J. Ind. Intell. Inf. Vol"},{"issue":"22","key":"10.3233\/MGS-200330_ref66","doi-asserted-by":"crossref","first-page":"32565","DOI":"10.1007\/s11042-019-08033-x","article-title":"Impact of compressed and down-scaled training images on vehicle detection in remote sensing imagery","volume":"78","author":"Karim","year":"2019","journal-title":"Multimed. Tools Appl."},{"key":"10.3233\/MGS-200330_ref67","doi-asserted-by":"crossref","unstructured":"R. Girshick, J. Donahue, T. Darrell and J. Malik, Rich feature hierarchies for accurate object detection and semantic segmentation, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (Columbus, Ohio), 2014, pp. 580\u2013587.","DOI":"10.1109\/CVPR.2014.81"},{"key":"10.3233\/MGS-200330_ref68","unstructured":"S. Ren, K. He, R. Girshick and J. Sun, Faster R-CNN: Towards real-time object detection with region proposal networks, in: Advances in Neural Information Processing Systems, 2015, pp. 91\u201399."},{"key":"10.3233\/MGS-200330_ref69","doi-asserted-by":"crossref","unstructured":"K. He, G. Gkioxari, P. Doll\u00e1r and R. Girshick, Mask r-cnn, in: Computer Vision (ICCV), 2017 IEEE International Conference on, 2017, pp. 2980\u20132988.","DOI":"10.1109\/ICCV.2017.322"},{"issue":"4","key":"10.3233\/MGS-200330_ref70","doi-asserted-by":"crossref","first-page":"312","DOI":"10.3390\/rs9040312","article-title":"Deep learning approach for car detection in UAV imagery","volume":"9","author":"Ammour","year":"2017","journal-title":"Remote Sens."},{"issue":"20","key":"10.3233\/MGS-200330_ref71","doi-asserted-by":"crossref","first-page":"21651","DOI":"10.1007\/s11042-016-4043-5","article-title":"Vehicle detection from high-resolution aerial images using spatial pyramid pooling-based deep convolutional neural networks","volume":"76","author":"Qu","year":"2017","journal-title":"Multimed. Tools Appl."},{"key":"10.3233\/MGS-200330_ref72","doi-asserted-by":"crossref","first-page":"106964","DOI":"10.1016\/j.patcog.2019.106964","article-title":"Rotated cascade R-CNN: A shape robust detector with coordinate regression","volume":"96","author":"Zhu","year":"2019","journal-title":"Pattern Recognit."},{"key":"10.3233\/MGS-200330_ref73","doi-asserted-by":"crossref","unstructured":"Z. Cai and N. Vasconcelos, Cascade r-cnn: Delving into high quality object detection, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (Salt Lake City, UT, USA), 2018, pp. 6154\u20136162.","DOI":"10.1109\/CVPR.2018.00644"},{"key":"10.3233\/MGS-200330_ref75","doi-asserted-by":"crossref","unstructured":"S. Karim, Y. Zhang, S. Yin and M.R. Asif, An Efficient Region Proposal Method for Optical Remote Sensing Imagery, in: IGARSS 2018\u20132018 IEEE International Geoscience and Remote Sensing Symposium, 2018, pp. 2455\u20132458.","DOI":"10.1109\/IGARSS.2018.8518098"},{"key":"10.3233\/MGS-200330_ref76","doi-asserted-by":"crossref","unstructured":"Z. Zhong, L. Jin and Z. Xie, High performance offline handwritten chinese character recognition using googlenet and directional feature maps, in: 2015 13th International Conference on Document Analysis and Recognition (ICDAR), 2015, pp. 846\u2013850.","DOI":"10.1109\/ICDAR.2015.7333881"},{"key":"10.3233\/MGS-200330_ref77","doi-asserted-by":"crossref","unstructured":"J. Redmon and A. Farhadi, YOLO9000: better, faster, stronger, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (Honolulu, HI, USA), 2017, pp. 7263\u20137271.","DOI":"10.1109\/CVPR.2017.690"},{"issue":"4","key":"10.3233\/MGS-200330_ref78","doi-asserted-by":"crossref","first-page":"127","DOI":"10.3390\/a10040127","article-title":"A real-time chinese traffic sign detection algorithm based on modified YOLOv2","volume":"10","author":"Zhang","year":"2017","journal-title":"Algorithms"},{"key":"10.3233\/MGS-200330_ref80","doi-asserted-by":"crossref","unstructured":"D. Erhan, C. Szegedy, A. Toshev and D. Anguelov, Scalable object detection using deep neural networks, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (Portland, Oregon), 2014, pp. 2147\u20132154.","DOI":"10.1109\/CVPR.2014.276"},{"issue":"6","key":"10.3233\/MGS-200330_ref81","doi-asserted-by":"crossref","first-page":"631","DOI":"10.3390\/rs11060631","article-title":"R-CNN-Based Ship Detection from High Resolution Remote Sensing Imagery","volume":"11","author":"Zhang","year":"2019","journal-title":"Remote Sens."}],"container-title":["Multiagent and Grid Systems"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/MGS-200330","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T05:26:50Z","timestamp":1777613210000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/MGS-200330"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10,30]]},"references-count":75,"journal-issue":{"issue":"3"},"URL":"https:\/\/doi.org\/10.3233\/mgs-200330","relation":{},"ISSN":["1875-9076","1574-1702"],"issn-type":[{"value":"1875-9076","type":"electronic"},{"value":"1574-1702","type":"print"}],"subject":[],"published":{"date-parts":[[2020,10,30]]}}}