[1] 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, 39, 1137-1149(2017).

[2] [2] 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 Press, 2016: 779- 788.

[3] Liu W, Anguelov D, Erhan D 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, 9905, 21-37(2016).

[4] Ji Z, Kong Q K, Wang J. Object detection algorithm guided by dual attention models[J]. Laser & Optoelectronics Progress, 57, 061008(2020).

[5] Lin T Y, Dollár P, Girshick R et al. Feature pyramid networks for object detection[C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA., 936-944(2017).

[6] Singh B, Davis L S. An analysis of scale invariance in object detection-SNIP[C]∥2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA., 3578-3587(2018).

[7] Singh B, Najibi M, Davis L S. SNIPER efficient multi-scale training[C]∥2018 Annual Conference on Neural Information Processing Systems, December 3-8, 2018, Montreal, Canada., 9333-9343(2018).

[8] Zhou P, Ni B B, Geng C et al. Scale-transferrable object detection[C]∥2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA., 528-537(2018).

[9] Wang W F, Jin J, Chen J M. Rapid detection algorithm for small objects based on receptive field block[J]. Laser & Optoelectronics Progress, 57, 021501(2020).

[10] Liu J M, Yang S, Huang H. Hyperspectral remote sensing image classification based on local reconstruction Fisher analysis[J]. Chinese Journal of Lasers, 47, 0710001(2020).

[11] Song L, Cheng Y M, Zhao Y Q. Hyper-spectrum classification based on sparse representation model and auto-regressive model[J]. Acta Optica Sinica, 32, 0330003(2012).

[12] Zhang Y S, Wu L, Ren H Z et al. Mapping water quality parameters in urban rivers from hyperspectral images using a new self-adapting selection of multiple artificial neural networks[J]. Remote Sensing, 12, 336(2020).

[13] Khan F U, Guarnizo G, Martín-Mateos P. Direct hyperspectral dual-comb gas imaging in the mid-infrared[J]. Optics Letters, 45, 5335-5338(2020).

[14] Yokoya N. Chan J C W, Segl K. Potential of resolution-enhanced hyperspectral data for mineral mapping using simulated EnMAP and sentinel-2 images[J]. Remote Sensing, 8, 172(2016).

[15] Malkamäki T, Kaasalainen S, Ilinca J. Portable hyperspectral lidar utilizing 5 GHz multichannel full waveform digitization[J]. Optics Express, 27, A468-A480(2019).

[16] Liu L X, He D, Li M Z et al. Identification of Xinjiang jujube varieties based on hyperspectral technique and machine learning[J]. Chinese Journal of Lasers, 47, 1111002(2020).

[17] Medus L D, Saban M. Francés-Víllora J V, et al. Hyperspectral image classification using CNN: application to industrial food packaging[J]. Food Control, 125, 107962(2021).

[18] Liu P, Zeng Z G, Wang J. Multistability analysis of a general class of recurrent neural networks with non-monotonic activation functions and time-varying delays[J]. Neural Networks, 79, 117-127(2016).

[19] Hornik K. Approximation capabilities of multilayer feedforward networks[J]. Neural Networks, 4, 251-257(1991).

[20] Ramachandran P, Zoph B. -10-16)[2021-04-10]. https:∥arxiv., org/abs/1710, 05941(2017).

[22] Hu J, Shen L, Sun G. Squeeze-and-excitation networks[C]∥2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA., 7132-7141(2018).

[23] Zhang X D, Wang T J, Zhu S J et al. Hyperspectral image classification based on dilated convolutional attention neural network[J]. Acta Optica Sinica, 41, 0310001(2021).

[24] Zhou X Y, Wang D Q. -05-16)[2021-04-10]. https:∥arxiv., org/abs/1904, 07850(2019).

[25] Redmon J. -05-08)[2021-04-10]. https:∥arxiv., org/abs/1804, 02767(2018).

[26] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition[C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA., 770-778(2016).

[27] Dai J F, Qi H Z, Xiong Y W et al. Deformable convolutional networks[C]∥2017 IEEE International Conference on Computer Vision (ICCV), October 22-29, 2017, Venice, Italy., 764-773(2017).

[28] Law H, Deng J. CornerNet: detecting objects as paired keypoints[M]. ∥Ferrari V, Hebert M, Sminchisescu C, et al. Computer vision-ECCV 2018. Lecture notes in computer science. Cham: Springer, 11218, 765-781(2018).

[29] Chao Y W, Vijayanarasimhan S, Seybold B et al. Rethinking the faster R-CNN architecture for temporal action localization[C]∥2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA., 1130-1139(2018).

[30] Yang L, Peng H W, Zhang D W et al. Revisiting anchor mechanisms for temporal action localization[J]. IEEE Transactions on Image Processing, 29, 8535-8548(2020).

[31] Lin T Y, Maire M, Belongie S et al. Microsoft COCO: common objects in context[M]. ∥Fleet D, Pajdla T, Schiele B, et al. Computer vision-ECCV 2014. Lecture notes in computer science. Cham: Springer, 8693, 740-755(2014).

[32] [32] TianZ, Shen CH, ChenH, et al.FCOS: fully convolutional one-stage object detection[C]∥2019 IEEE/CVF International Conference on Computer Vision (ICCV), October 27-November 2, 2019, Seoul, Korea (South). New York: IEEE Press, 2019: 9626- 9635.