[1] 薛猛,周学文,孔维亮.反无人机系统研究现状及关键技术分析[J].飞航导弹,2021(5):52-56,60. XUE M,ZHOU X W,KONG W L.Research status and key technology analysis of anti-UAV system[J].Aerodynamic Missile Journal,2021(5):52-56,60.(in Chinese) [2] CHEN Y,MENG H B,WEN X L,et al.Classification methods of a small sample target object in the sky based on the higher layer visualizing feature and transfer learning deep networks[J].EURASIP Journal on Wireless Communications and Networking,2018(1):127. [3] AKER C,KALKAN S.Using deep networks for drone detection[C]∥Proceedings of the 14th IEEE International Conference on Advanced Video and Signal Based Surveillance.Lecce,Italy:IEEE,2017. [4] 梁杰,任君,李磊,等.基于典型几何形状精确回归的机场跑道检测方法[J].兵工学报,2020,41(10):2045-2054. LIANG J,REN J,LI L,et al.Airport runway detection algorithm based on accurate regression of typical geometric shapes[J].Acta Armamentarii,2020,41(10):2045-2054.(in Chinese) [5] 李秋珍,熊饶饶,王汝鹏,等.基于SSD算法的实时无人机识别方法研究[J].舰船电子工程,2019,39(5):30-35. LI Q Z,XIONG R R,WANG R P,et al.Research on real-time UAV recognition method based on SSD algorithm[J].Ship Electronic Engineering,2019,39(5):30-35.(in Chinese) [6] 王若霄,徐智勇,张建林.基于SSD的实时轻量级无人机检测算法[J].半导体光电,2020,41(2):296-300. WANG R X,XU Z Y,ZHANG J L.Real-time lightweight UAV detection method based on SSD algorithm[J].Semiconductor Optoelectronics,2020,41(2):296-300.(in Chinese) [7] 侯鑫,曲国远,魏大洲,等.基于迭代稀疏训练的轻量化无人机目标检测算法[J/OL].计算机研究与发展:1-11[2022-01-10].http:∥kns.cnki.net/kcms/detail/11.1777.TP.20210527.1106.002.html. HOU X,QU G Y,WEI D Z,et al.A lightweight UAV object detection algorithm based on iterative sparse training[J/OL].Journal of Computer Research and Development:1-11[2022-01-10].http:∥kns.cnki.net/kcms/detail/11.1777.TP.20210527.1106.002.html.(in Chinese) [8] 孔维刚,李文婧,王秋艳,等.基于改进YOLOv4算法的轻量化网络设计与实现[J].计算机工程,2022,48(3):181-188. KONG W G,LI W J,WANG Q Y,et al.Design and implementation of lightweight network based on YOLOv4 algorithm[J].Computer Engineering,2022,48(3):181-188. (in Chinese) [9] HEREDIA A.BARROS-GAVILANES G.Video processing inside embedded devices using SSD-Mobilenet to count mobility actors[C]∥Proceedings of the IEEE Colombian Conference on Application in Computational Intelligence.Barranquilla,Colombia:IEEE,2019. [10] ALI H.KHURSHEED M,FATIMA S K,SHUJA S M,et al.Object recognition for dental instruments using SSD-MobileNet[C]∥Proceedings of the 2019 International Conference on Information Science and Communication Technology.Karachi,Pakistan:IEEE,2019. [11] GIRSHICK R,DONAHUE J,DARRELL T,et al.Rich feature hierarchies for accurate object detection and semantic segmentation[C]∥Proceedings of IEEE Confrence on Computer Vision and Pattern Recognition.Columbus,OH,US:IEEE,2014:580-587. [12] GIRSHICK R.Fast R-CNN[C]∥Proceedings of IEEE International Conference on Computer Vision.Santiago,Chile:IEEE,2015:1440-1448. [13] REN S Q,HE K M,GIRSHICK R,et al.Faster R-CNN: towards real-time object detection with region proposal networks[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2017,39(6):1137-1149. [14] REDMON J,DIVVALA S,GIRSHICK R.You only look once: unified,real-time object detection[C]∥Proceedings of IEEE Confrence on Computer Vision and Pattern Recognition.Las Vegas,NV,US:IEEE,2016. [15] LIU W,ANGUELOV D,ERHAN D,et al.SSD:single shot multibox detector[C]∥Proceedings of the European Conference on Computer Vision.Amsterdam,the Netherlands:Springer,2016:21-37. [16] 文华.华为昇腾智能制造使能平台加速工业制造智能升级[J].通信世界,2020(32):30-31. WEN H.Huawei Centem intelligent manufacturing enables the platform to accelerate the intelligent upgrading of industrial manufacturing[J].Communications World,2020(32):30-31.(in Chinese) [17] 吴中勤,韩钧宇.如何研发中国新一代自主可控的AI算法引擎[J].人工智能,2019:112-121. WU Z Q,HAN J Y.How to develop the new generation of autonomous and controllable AI algorithm engine in China[J].Artificial Intelligence VIEW,2019:112-121.(in Chinese) [18] ABADI M,BARHAM P,CHEN J,et al.TensorFlow: a system for large-scale machine learning[C]∥Proceedings of the 12nd USENIX Symposium on Operating Systems Design and Implementation.Savannah,GA,US:USENIX,2016:265-283. [19] PASZKE A,GROSS S,MASSA F,et al.PyTorch:An imperative style,high-performance deep learning library[C]∥Proceedings of the 33rd Conference on Neural Information Processing Systems.Vancouver,Canada:Vancouver Convention Centre,2019:8026-8037. [20] JIA Y P,SHELHAMER E,DONAHUE J,et al.Caffe: Convolutional architecture for fast feature embedding[C]∥Proceedings of the 22nd ACM International Conference on Multimedia.Orlando,FL,US:ACM,2014: 675-678. [21] CHEN T Q,LI M,LI Y T,et al.MXNet:a flexible and efficient machine learning library for heterogeneous distributed systems[J/OL].arXiv:1-6.(2015-12-02).http:∥www.cs.cmu.edu/~muli/file/mxnet-learning-sys.pdf. [22] 马艳军,于佃海,吴甜,等.飞桨:源于产业实践的开源深度学习平台[J].数据与计算发展前沿,2019,1(1):105-115. MA Y J,YU D H,WU T,et al.PaddlePaddle:an open-source deep learning platform from industrial practice[J].Frontiers of Data & Computing,2019,1(1):105-115.(in Chinese) [23] 于璠.新一代深度学习框架研究[J].大数据,2020,6(4):69-80. YU F.Research on the next-generation deep learning framework[J].Big Data Research,2020,6(4):69-80.(in Chinese) [24] HOWARD A G,ZHU M L,CHEN B,et al.Mobilenets:efficient convolutional neural networks for mobile vision applications[EB/OL].Computer Science,2017. https:∥arxiv.org/abs/1704.04861. [25] SVANSTROM F,ENGLUND C,ALONSO-FERNANDEZ F. Real-time drone detection and tracking with visible,thermal and acoustic sensors[C]∥Proceedings of the 25th International Conference on Pattern Recognition.Milan,Italy:IEEE,2021.