[1] Lin S, Zhao Y. Review on key technologies of target exploration in underwater optical images[J]. Laser & Optoelectronics Progress, 57, 060002(2020).

[2] 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).

[3] He K M, Gkioxari G, Dollar P et al. Mask R-CNN[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42, 386-397(2020).

[4] Dai J F, Li Y, He K M et al. R-FCN: object detection via region-based fully convolutional networks[EB/OL]. (2016-05-20)[2020-10-15]. https://arxiv.org/abs/1605.06409v1

[5] Bochkovskiy A, Wang C Y, Liao H Y. YOLOv4: optimal speed and accuracy of object detection[EB/OL]. (2020-04-23)[2020-07-01]. https://arxiv.org/abs/2004.10934

[6] Redmon J, Farhadi A. YOLO9000: better, faster, stronger[C]. //2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA, 6517-6525(2017).

[7] Redmon J, Farhadi A. YOLOv3: an incremental improvement[EB/OL]. (2018-04-08)[2020-07-01]. https://arxiv.org/abs/1804.02767

[8] 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, 9905, 21-37(2016).

[9] Lin T Y, Goyal P, Girshick R et al. Focal loss for dense object detection[C]. //2017 IEEE International Conference on Computer Vision (ICCV), October 22-29, 2017, Venice, Italy., 2999-3007(2017).

[10] 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).

[11] Huang G, Liu Z, van der Maaten L et al. Densely connected convolutional networks[C]. //2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA., 2261-2269(2017).

[12] Chen Y, Li J, Xiao H et al. Dual path networks[EB/OL]. (2017-08-01)[2020-07-01]. https://arxiv.org/abs/1707.01629

[13] Goel A, Tung C, Lu Y H et al. A survey of methods for low-power deep learning and computer vision[C]. //2020 IEEE 6th World Forum on Internet of Things (WF-IoT), June 2-16, 2020, New Orleans, LA, USA., 1-6(2020).

[14] Anwar S, Sung W Y. Coarse pruning of convolutional neural networks with random masks[EB/OL]. (2016-10-30)[2020-07-01]. https://arxiv.org/abs/1610.09639v1.

[15] Yin W F, Liang L Y, Peng H M et al. Research progress on convolutional neural network compression and acceleration technology[J]. Computer Systems & Applications, 29, 16-25(2020).

[16] Hinton G, Vinyals O, Dean J. Distilling the knowledge in a neural network[EB/OL]. (2015-03-09)[2020-07-01]. https://arxiv.org/abs/1503.02531

[17] Liu Z, Li J G, Shen Z Q et al. Learning efficient convolutional networks through network slimming[C]. //2017 IEEE International Conference on Computer Vision (ICCV), October 22-29, 2017, Venice, Italy., 2755-2763(2017).

[18] Lin Y, Tu Y, Dou Z. An improved neural network pruning technology for automatic modulation classification in edge devices[J]. IEEE Transactions on Vehicular Technology, 69, 5703-5706(2020).

[19] Liu K, Li X J. De-hazing and enhancement methods for underwater and low-light images[J]. Acta Optica Sinica, 40, 1910003(2020).

[20] Iqbal K, Odetayo M, James A et al. Enhancing the low quality images using unsupervised colour correction method[C]. //2010 IEEE International Conference on Systems, Man and Cybernetics, October 10-13, 2010, Istanbul, Turkey., 1703-1709(2010).

[21] Drews P, do Nascimento E, Moraes F et al. Transmission estimation in underwater single images[C]. //2013 IEEE International Conference on Computer Vision Workshops, December 2-8, 2013, Sydney, NSW, Australia., 825-830(2013).

[22] Wang X Q, Wang X J. Real-time target detection method applied to embedded graphic processing unit[J]. Acta Optica Sinica, 39, 0315005(2019).