激光与光电子学进展, 2019, 56 (16): 161502, 网络出版: 2019-08-05   

基于YOLO v3的红外末制导典型目标检测 下载: 1379次

Typical Target Detection for Infrared Homing Guidance Based on YOLO v3
作者单位
火箭军工程大学作战保障学院, 陕西 西安 710025
引用该论文

陈铁明, 付光远, 李诗怡, 李源. 基于YOLO v3的红外末制导典型目标检测[J]. 激光与光电子学进展, 2019, 56(16): 161502.

Tieming Chen, Guangyuan Fu, Shiyi Li, Yuan Li. Typical Target Detection for Infrared Homing Guidance Based on YOLO v3[J]. Laser & Optoelectronics Progress, 2019, 56(16): 161502.

参考文献

[1] 姜锦锋. 红外图像的目标检测、识别与跟踪技术研究[D]. 西安: 西北工业大学, 2004.

    Jiang JF. Research on target detection, recognition and tracking technology of infrared image [D]. Xi'an:Northwestern Polytechnical University, 2004.

[2] 张天序, 王岳环, 钟胜. 飞行器光学寻的制导信息处理技术[M]. 北京: 国防工业出版社, 2014.

    Zhang TX, Wang YH, ZhongS. Guidance information processing methods in aircraft optical imaging seeker[M]. Beijing: National Defense Industry Press, 2014.

[3] 姚广顺, 孙韶媛, 方建安, 等. 基于红外与雷达的夜间无人车场景深度估计[J]. 激光与光电子学进展, 2017, 54(12): 121003.

    Yao G S, Sun S Y, Fang J A, et al. Depth estimation of night driverless vehicle scene based on infrared and radar[J]. Laser & Optoelectronics Progress, 2017, 54(12): 121003.

[4] 易翔, 王炳健. 基于多特征的快速红外弱小目标检测算法[J]. 光子学报, 2017, 46(6): 0610002.

    Yi X, Wang B J. Fast infrared and dim target detection algorithm based on multi-feature[J]. Acta Photonica Sinica, 2017, 46(6): 0610002.

[5] 熊斌, 黄心汉, 王敏. 基于自适应目标图像恢复的红外弱小目标检测[J]. 华中科技大学学报(自然科学版), 2017, 45(10): 25-30.

    Xiong B, Huang X H, Wang M. Infrared dim small target detection based on adaptive target image recovery[J]. Journal of Huazhong University of Science and Technology(Natural Science Edition), 2017, 45(10): 25-30.

[6] Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM, 2017, 60(6): 84-90.

[7] SermanetP, EigenD, ZhangX, et al. OverFeat: integrated recognition, localization and detection using convolutionalnetworks[J/OL].( 2014-02-24)[2019-01-06]. https:∥arxiv.org/abs/1312. 6229.

[8] 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.

[9] RedmonJ, DivvalaS, GirshickR, et al. You only look once: unified, real-time object detection[C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA. New York: IEEE, 2016: 779- 788.

[10] RedmonJ, FarhadiA. YOLO9000: better, faster, stronger[C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA. New York: IEEE, 2017: 6517- 6525.

[11] RedmonJ, Farhadi A. YOLOv3: an incremental improvement[J/OL].( 2018-04-08)[2019-01-09]. https:∥arxiv.org/abs/1804. 02767.

[12] 魏湧明, 全吉成, 侯宇青阳. 基于YOLO v2的无人机航拍图像定位研究[J]. 激光与光电子学进展, 2017, 54(11): 111002.

    Wei Y M, Quan J C, Houyu Q Y. Aerial image location of unmanned aerial vehicle based on YOLO v2[J]. Laser & Optoelectronics Progress, 2017, 54(11): 111002.

[13] Ruder S. An overview of gradient descent optimization algorithms[J/OL].( 2017-06-15)[2019-01-05]. https:∥arxiv.org/abs/1609. 04747.

[14] KingmaD, Ba J J C S. Adam: a method for stochastic optimization[J/OL].( 2017-01-30)[2019-01-06]. https:∥arxiv.org/abs/1412. 6980.

[15] 张晋晶. 基于随机梯度下降的神经网络权重优化算法[D]. 重庆: 西南大学, 2018.

    Zhang JJ. Optimization algorithms of neural networks weights based on stochastic gradient descent[D]. Chongqing: Southwest University, 2018.

陈铁明, 付光远, 李诗怡, 李源. 基于YOLO v3的红外末制导典型目标检测[J]. 激光与光电子学进展, 2019, 56(16): 161502. Tieming Chen, Guangyuan Fu, Shiyi Li, Yuan Li. Typical Target Detection for Infrared Homing Guidance Based on YOLO v3[J]. Laser & Optoelectronics Progress, 2019, 56(16): 161502.

本文已被 2 篇论文引用
被引统计数据来源于中国光学期刊网
引用该论文: TXT   |   EndNote

相关论文

加载中...

关于本站 Cookie 的使用提示

中国光学期刊网使用基于 cookie 的技术来更好地为您提供各项服务,点击此处了解我们的隐私策略。 如您需继续使用本网站,请您授权我们使用本地 cookie 来保存部分信息。
全站搜索
您最值得信赖的光电行业旗舰网络服务平台!