激光与光电子学进展, 2020, 57 (12): 121022, 网络出版: 2020-06-03   

基于Faster R-CNN金丝猴优化检测方法

Optimized Detection Method for Snub-Nosed Monkeys Based on Faster R-CNN
作者单位
中国林业科学研究院资源信息研究所,北京 100091
引用该论文

孙蕊, 张旭, 郭颖, 于新文, 陈艳, 侯亚男. 基于Faster R-CNN金丝猴优化检测方法[J]. 激光与光电子学进展, 2020, 57(12): 121022.

孙蕊, 张旭, 郭颖, 于新文, 陈艳, 侯亚男. Optimized Detection Method for Snub-Nosed Monkeys Based on Faster R-CNN[J]. Laser & Optoelectronics Progress, 2020, 57(12): 121022.

参考文献

[1] 李明, 向左甫. 中国旗舰保护物种: 金丝猴[J]. 世界环境, 2016( S1): 12- 15.

    LiM, Xiang ZF. Flagship species under China's protection: golden monkeys[J]. World Environment, 2016( S1): 12- 15.

[2] . 金丝猴生态的初步研究[J]. 动物学研究, 1982, 3(2): 105-110.

    . Preliminary studies on the ecology of the golden-haired monkey[J]. Zoological Research, 1982, 3(2): 105-110.

[3] , et al. Genetic diversity of the Sichuan snub-nosed monkey (Rhinopithecus roxellana) in Shennongjia National Park, China using RAD-seq analyses[J]. Genetica, 2019, 147(3/4): 327-335.

[4] , et al. Epigenetic and transcriptional signatures of ex situ conserved golden snub-nosed monkeys (Rhinopithecus roxellana)[J]. Biological Conservation, 2019, 237: 175-184.

[5] 穆俊明. 神农架保护区金丝猴生境退化机制及恢复技术[D]. 武汉: 华中农业大学, 2015: 34- 35.

    Mu JM. The habitat degradation and recovery technology of the golden snub-nosed monkey in Shennongjia nature reserve[D]. Wuhan: Huazhong Agricultural University, 2015: 34- 35.

[6] , 等. 湖北川金丝猴现状及保护研究[J]. 野生动物学报, 2019, 40(3): 602-609.

    , et al. Protection and status of Sichuan snub-nosed monkey in Hubei Province[J]. Chinese Journal of Wildlife, 2019, 40(3): 602-609.

[7] , et al. Congruence between arthropod and plant diversity in a biodiversity hotspot largely driven by underlying abiotic factors[J]. Ecological Applications, 2019, 29(4): e01883.

[8] . The effects of anthropogenic landscape change on the abundance and habitat use of terrestrial large mammals of Nech Sar National Park[J]. Environmental Systems Research, 2019, 88(1): 1-16.

[9] , 等. 图像理解中的卷积神经网络[J]. 自动化学报, 2016, 42(9): 1300-1312.

    , et al. Convolutional neural networks in image understanding[J]. Acta Automatica Sinica, 2016, 42(9): 1300-1312.

[10] 方楠. 基于CNN的金丝猴面部识别研究与实现[D]. 西安: 西安电子科技大学, 2017: 36- 37.

    FangN. Research and implement of gold monkey recognition based on convolutional neural network[D]. Xi'an:Xidian University, 2017: 36- 37.

[11] 张飞飞. 复杂背景下的多姿态猴脸检测与识别方法研究[D]. 西安: 西安理工大学, 2018: 11- 12.

    Zhang FF. Research on methods of detection and recognition of multi-pose monkey face in complex background[D]. Xi'an: Xi'an University ofTechnology, 2018: 11- 12.

[12] 范莹莹. 基于深度学习的金丝猴面部识别软件设计与实现[D]. 西安: 西安电子科技大学, 2018: 35- 36.

    Fan YY. Design and implementation of golden monkey face recognition software based on deep learning[D]. Xi'an:Xidian University, 2018: 35- 36.

[13] 王革伟. 川金丝猴面部识别方法研究[D]. 西安: 西北大学, 2018: 27- 28.

    Wang GW. The research on face recognition of Sichuan golden monkey[D]. Xi'an:Northwest University, 2018: 27- 28.

[14] , 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.

[15] LanG, YoshuaB, AaronC. Deep learning[M]. Beijing: Posts & Telecom Press, 2017: 412- 413.

[16] , 等. 蝴蝶种类自动识别研究[J]. 计算机研究与发展, 2018, 55(8): 1609-1618.

    , et al. The automatic identification of butterfly species[J]. Journal of Computer Research and Development, 2018, 55(8): 1609-1618.

[17] , 等. 基于Faster R-CNN的榆紫叶甲虫识别方法研究[J]. 计算机工程与应用, 2018, 54(23): 89-93, 108.

    , et al. Identification method of ambrostoma quadriimpressum motschlsky based on Faster R-CNN[J]. Computer Engineering and Applications, 2018, 54(23): 89-93, 108.

[18] GirshickR, DonahueJ, DarrellT, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]∥2014 IEEE Conference on Computer Vision and Pattern Recognition, June 23-28, 2014, Columbus, OH, USA. IEEE, 2014: 580- 587.

[19] GirshickR. Fast R-CNN[C]∥2015 IEEE International Conference on Computer Vision (ICCV), December 7-13, 2015, Santiago, Chile. IEEE, 2015: 1440- 1448.

[20] , et al. Deep learning model design of video target tracking based on TensorFlow platform[J]. Laser & Optoelectronics Progress, 2017, 54(9): 091501.

[21] . Hoi S C H. Face detection using deep learning: an improved faster RCNN approach[J]. Neurocomputing, 2018, 299: 42-50.

[22] , et al. Mean Normalized Retrieval Order (MNRO): a new content-based image retrieval performance measure[J]. Multimedia Tools and Applications, 2014, 70(3): 1767-1798.

[23] , 等. 基于改进的Faster R-CNN目标检测算法[J]. 激光与光电子学进展, 2019, 57(10): 101009.

    , et al. Based on improved Faster R-CNN target detection algorithm[J]. Laser & Optoelectronics Progress, 2019, 57(10): 101009.

[24] . 基于深度学习的目标检测与可行域分割研究[J]. 激光与光电子学进展, 2019, 57(12): 121013.

    . Research on target detection and feasible region segmentation based on deep learning[J]. Laser & Optoelectronics Progress, 2019, 57(12): 121013.

[25] , 等. 基于Faster R-CNN深度网络的油菜田间杂草识别方法[J]. 激光与光电子学进展, 2019, 57(2): 021508.

    , et al. Identification method of weeds in rapeseed field based on Faster R-CNN deep network[J]. Laser & Optoelectronics Progress, 2019, 57(2): 021508.

孙蕊, 张旭, 郭颖, 于新文, 陈艳, 侯亚男. 基于Faster R-CNN金丝猴优化检测方法[J]. 激光与光电子学进展, 2020, 57(12): 121022. 孙蕊, 张旭, 郭颖, 于新文, 陈艳, 侯亚男. Optimized Detection Method for Snub-Nosed Monkeys Based on Faster R-CNN[J]. Laser & Optoelectronics Progress, 2020, 57(12): 121022.

本文已被 1 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!