一种应用于光遗传激光投影系统的目标检测算法 下载: 923次
史再峰, 叶鹏, 孙诚, 罗韬, 王汉杰, 潘惠卓. 一种应用于光遗传激光投影系统的目标检测算法[J]. 激光与光电子学进展, 2020, 57(6): 061503.
Zaifeng Shi, Peng Ye, Cheng Sun, Tao Luo, Hanjie Wang, Huizhuo Pan. Object Detection Algorithm Applied to Optical Genetic Laser Projection System[J]. Laser & Optoelectronics Progress, 2020, 57(6): 061503.
[2] Boyden E S, Zhang F, Bamberg E, et al. Millisecond-timescale, genetically targeted optical control of neural activity[J]. Nature Neuroscience, 2005, 8(9): 1263-1268.
[3] Zhang F, Gradinaru V, Adamantidis A R, et al. Optogenetic interrogation of neural circuits: technology for probing mammalian brain structures[J]. Nature Protocols, 2010, 5(3): 439-456.
[4] Wang Y, Lin X D, Chen X, et al. Tetherless near-infrared control of brain activity in behaving animals using fully implantable upconversion microdevices[J]. Biomaterials, 2017, 142: 136-148.
[5] 龙鑫, 苏寒松, 刘高华, 等. 一种基于角度距离损失函数和卷积神经网络的人脸识别算法[J]. 激光与光电子学进展, 2018, 55(12): 121505.
[6] 欧攀, 张正, 路奎, 等. 基于卷积神经网络的遥感图像目标检测[J]. 激光与光电子学进展, 2019, 56(5): 051002.
[7] 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.
[8] 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.
[9] LiuW, AnguelovD, ErhanD, et al. SSD: single shot MultiBox detector[M] ∥Leibe B, Matas J, Sebe N, et al. Computer vision-ECCV 2016. Lecture notes in computer science. Cham: Springer, 2016, 9905: 21- 37.
[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] Tang X L, Yang W, Hu X S, et al. A novel simplified model for torsional vibration analysis of a series-parallel hybrid electric vehicle[J]. Mechanical Systems and Signal Processing, 2017, 85: 329-338.
[12] RedmonJ, Farhadi A. Yolov3: an incremental improvement[EB/OL]. ( 2018-04-08)[2018-09-07]. https:∥arxiv.org/abs/1804. 02767.
[13] HuangG, Liu S C, van der Maaten L, et al. CondenseNet: an efficient DenseNet using learned group convolutions[C]∥2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA. New York: IEEE, 2018: 2752- 2761.
[14] Zhang XY, Zhou XY, Lin MX, et al. ShuffleNet: an extremely efficient convolutional neural network for mobile devices[C]∥2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA. New York: IEEE, 2018: 6848- 6856.
[15] Lin T Y, Goyal P, Girshick R, et al. Focal loss for dense object detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 1.
史再峰, 叶鹏, 孙诚, 罗韬, 王汉杰, 潘惠卓. 一种应用于光遗传激光投影系统的目标检测算法[J]. 激光与光电子学进展, 2020, 57(6): 061503. Zaifeng Shi, Peng Ye, Cheng Sun, Tao Luo, Hanjie Wang, Huizhuo Pan. Object Detection Algorithm Applied to Optical Genetic Laser Projection System[J]. Laser & Optoelectronics Progress, 2020, 57(6): 061503.