468 工程科学学报,第42卷,第4期 Vegas,,2016:4293 unified,real-time object detection /Proceedings of the IEEE [7]Zhang D.Maei H.Wang X,et al.Deep reinforcement learning for Conference on Computer Vision and Pattern Recognition.Las visual object tracking in videos[J/OL].arXiv preprint (2017-04- Vegas,2016:779 10)[2019-09-10.https://arxiv.org/abs/1701.08936 [17]Coluccia A,Fascista A,Schumann A,et al.Drone-vs-Bird [8]Xi X.Yu Z.Zhan Z,et al.Multi-task cost-sensitive-convolutional detection challenge at IEEE AVSS2019//2019 16th IEEE neural network for car detection./EEE Access,2019,7:98061 International Conference on Advanced Video and Signal Based [9] Wu Y W,Sui Y,Wang G H.Vision-based real-time aerial object Surveillance (AVSS).Taipei,2019:1 localization and tracking for UAV sensing system.IEEE Access, [18]Liu H,Wei Z Q,Chen Y T,et al.Drone detection based on an 2017,5:23969 audio-assisted camera array 2017 IEEE Third International [10]Rozantsev A,Lepetit V,Fua P.Flying objects detection from a Conference on Multimedia Big Data (BigMM).Laguna Hills, single moving camera ll Proceedings of the IEEE Conference on 2017:402 Computer Vision and Pattern Recognition.Boston,2015:4128 [19]Mezei J,Fiaska V,Molnar A.Drone sound detection //2015 16th [11]Girshick R.Fast R-CNN /Proceedings of the IEEE International IEEE International Symposium on Computational Intelligence and Conference on Computer Vision.Santiago,2015:1440 Informatics (CINTD).Budapest,2015:333 [12]Ren S,He K,Girshick R,et al.Faster r-cnn:towards real-time [20]Nguyen P,Ravindranatha M,Nguyen A,et al.Investigating cost- object detection with region proposal networks /Advances in effective rf-based detection of drones /Proceedings of the 2nd Neural Information Processing Systems.Canada,2015:91 Workshop on Micro Aerial Vehicle Networks.Systems,and [13]Liu W.Anguelov D.Erhan D,et al.SSD:single shot multibox Applications for Civilian Use.Singapore,2016:17 detector /European Conference on Computer Vision.Amsterdam, [21]Lin T Y,Maire M,Belongie S,et al.Microsoft coco:common 2016:21 objects in context /European Conference on Computer Vision. [14]Redmon J,Farhadi A.Yolov3:an incremental Zurich,2014:740 improvement[J/OL].arXiv preprint (2018-04-08)[2019-09-10]. [22]Deng J.Dong W,Socher R,et al.Imagenet:a large-scale https://arxiv.org/abs/1804.02767 hierarchical image database/009 IEEE Conference on Computer [15]Redmon J.Farhadi A.YOLO9000:better,faster,stronger / Vision and Pattern Recognition.Miami,2009:248 Proceedings of the IEEE Conference on Computer Vision and [23]Kingma D P,Ba J.Adam:a method for stochastic Pattern Recognition.Honolulu,2017:7263 optimization[J/OL].arXiy preprint (2017-01-30)[2019-09-10]. [16]Redmon J,Divvala S,Girshick R,et al.You only look once: https://arxiv.org/abs/1412.6980Vegas, 2016: 4293 Zhang D, Maei H, Wang X, et al. Deep reinforcement learning for visual object tracking in videos[J/OL]. arXiv preprint (2017-04- 10)[2019-09-10]. https://arxiv.org/abs/1701.08936 [7] Xi X, Yu Z, Zhan Z, et al. Multi-task cost-sensitive-convolutional neural network for car detection. IEEE Access, 2019, 7: 98061 [8] Wu Y W, Sui Y, Wang G H. Vision-based real-time aerial object localization and tracking for UAV sensing system. IEEE Access, 2017, 5: 23969 [9] Rozantsev A, Lepetit V, Fua P. Flying objects detection from a single moving camera // Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Boston, 2015: 4128 [10] Girshick R. Fast R-CNN // Proceedings of the IEEE International Conference on Computer Vision. Santiago, 2015: 1440 [11] Ren S, He K, Girshick R, et al. Faster r-cnn: towards real-time object detection with region proposal networks // Advances in Neural Information Processing Systems. Canada, 2015: 91 [12] Liu W, Anguelov D, Erhan D, et al. SSD: single shot multibox detector // European Conference on Computer Vision. Amsterdam, 2016: 21 [13] Redmon J, Farhadi A. Yolov3: an incremental improvement[J/OL]. arXiv preprint (2018-04-08)[2019-09-10]. https://arxiv.org/abs/1804.02767 [14] Redmon J, Farhadi A. YOLO9000: better, faster, stronger // Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, 2017: 7263 [15] [16] Redmon J, Divvala S, Girshick R, et al. You only look once: unified, real-time object detection // Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, 2016: 779 Coluccia A, Fascista A, Schumann A, et al. Drone-vs-Bird detection challenge at IEEE AVSS2019// 2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). Taipei, 2019: 1 [17] Liu H, Wei Z Q, Chen Y T, et al. Drone detection based on an audio-assisted camera array // 2017 IEEE Third International Conference on Multimedia Big Data (BigMM). Laguna Hills, 2017: 402 [18] Mezei J, Fiaska V, Molnár A. Drone sound detection // 2015 16th IEEE International Symposium on Computational Intelligence and Informatics (CINTI). Budapest, 2015: 333 [19] Nguyen P, Ravindranatha M, Nguyen A, et al. Investigating costeffective rf-based detection of drones // Proceedings of the 2nd Workshop on Micro Aerial Vehicle Networks, Systems, and Applications for Civilian Use. Singapore, 2016: 17 [20] Lin T Y, Maire M, Belongie S, et al. Microsoft coco: common objects in context // European Conference on Computer Vision. Zurich, 2014: 740 [21] Deng J, Dong W, Socher R, et al. Imagenet: a large-scale hierarchical image database // 2009 IEEE Conference on Computer Vision and Pattern Recognition. Miami, 2009: 248 [22] Kingma D P, Ba J. Adam: a method for stochastic optimization[J/OL]. arXiv preprint (2017-01-30)[2019-09-10]. https://arxiv.org/abs/1412.6980 [23] · 468 · 工程科学学报,第 42 卷,第 4 期