[1] 高岩, 李波. 我国深海微生物资源研发现状、挑战与对策[J]. 生物资源, 2018, 40(1):13-17. DOI:10.14188/j.ajsh.2018.01.003. Gao Y, Li B. Advances and challenges in deep sea microbial resource research and development of China[J]. Biotic Resources, 2018, 40(1):13-17. DOI:10.14188/j.ajsh.2018.01.003. (in Chinese) [2] 刘峰, 刘予, 宋成兵, 等. 中国深海大洋事业跨越发展的三十年[J]. 中国有色金属学报, 2021, 31(10):2613-2623. DOI:10.11817/j.ysxb.1004.0609.2021-42112. Liu F, Liu Y, Song C B, et al. Three decades' development of China in deepsea field[J]. The Chinese Journal of Nonferrous Metals, 2021, 31(10):2613-2623. DOI:10.11817/j.ysxb.1004.0609.2021-42112. (in Chinese) [3] Otsu N. A threshold selection method from gray level histograms[J]. IEEE Transactions on Systems Man and Cybernetics, 1979, 9(1):62-66. [4] Canny J. A computational approach to edge detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1986, 8(6):679-698. [5] He Y, Zheng B, Ding Y, et al. Underwater image edge detection based on K-means algorithm[C]//2014 Oceans-St. John's Conference & Exhibition, IEEE, 2014:1-4. [6] 刘建思, 尹丽菊, 潘金凤, 等. 基于参数化对数图像处理模型的光照不均匀图像的边缘检测算法[J]. 激光与光电子学进展, 2021, 58(22):140-149.Liu J S, Yin L J, Pan J F, et al. Edge detection algorithm for unevenly inuminated images based on parameterized logarithmic image processing model[J]. Laser & Optoelectronics Progress, 2021, 58(22):140-149. (in Chinese) [7] 李庆忠, 刘晓丽, 谷娜娜. 水下人造目标线特征快速检测算法[J]. 计算机工程与应用, 2014, 50(11):180-183, 228. Li Q Z, Liu X L, Gu N N. Fast line detection algorithm for underwater man-made objects[J]. Computer Engineering and Applications, 2014, 50(11):180-183, 228. (in Chinese) [8] 颜明重, 黄冰逸, 朱大奇. 基于灰度波动的水下图像分割[J]. 哈尔滨工程大学学报, 2020, 41(9):1268-1273. Yan M Z, Huang B Y, Zhu D Q. Underwater image segmentation based on grayscale wave[J]. Journal of Harbin Engineering University, 2020, 41(9):1268-1273. (in Chinese) [9] 陈凯峰, 梁鉴如, 陈强, 等. 基于FPGA和CNN的水下目标识别系统[J]. 传感器与微系统, 2021, 40(4):103-105, 109. DOI:10.13873/J.1000-9787(2021)04-0103-03. Chen K F, Liang J R, Chen Q, et al. Underwater target recognition system based on FPGA and CNN[J]. Transducer and Microsystem Technologies, 2021, 40(4):103-105, 109. DOI:10.13873/J.1000-9787(2021)04-0103-03. (in Chinese) [10] 闫晓鹏. 离散小波变换在水下航行器目标图像降噪中的应用[J]. 舰船科学技术, 2021, 43(22):67-69. Yan X P. Application of discrete wavelet transform in underwater vehicle target image denoising[J]. Ship Science and Technology, 2021, 43(22):67-69. (in Chinese) [11] Dong X H, Li M H, Miao J S, et al. Edge detection operator for underwater target image[C]//2018 IEEE 3rd International Conference on Image, Vision and Computing, 2018:91-95. [12] 盛明伟, 唐松奇, 万磊, 等. 基于改进CNN-RANSAC的水下图像特征配准方法[J]. 计算机工程与科学, 2020, 42(5):859-868. Sheng M W, Tang S Q, Wan L, et al. An underwater image feature registration method based on improved CNN-RANSAC[J]. Computer Engineering & Science, 2020, 42(5):859-868. (in Chinese) [13] 宋绍剑, 朱靖旭. 基于Mask R-CNN和迁移学习的水下生物目标识别研究[J]. 计算机应用研究, 2020, 37(增刊2):386-388, 391. Song S J, Zhu J X. Object recognition research of underwater creature based on mask R-CNN and transfer learning[J]. Application Research of Computers, 2020, 37(Suppl.2):386-388, 391. (in Chinese) [14] Kamal S, Mohammed S K, Pillai P R S, et al. Deep learning architectures for underwater target recognition[C]//2013 International Symposium on Ocean Electronics, IEEE, 2013:48-54. [15] 刘有用, 张江梅, 王坤朋, 等. 不平衡数据集下的水下目标快速识别方法[J]. 计算机工程与应用, 2020, 56(17):236-242. Liu Y Y, Zhang J M, Wang K P, et al. Fast underwater target recognition with unbalanced data set[J]. Computer Engineering and Applications, 2020, 56(17):236-242. (in Chinese) [16] 刘波, 林焰, 王运龙. 水下图像边缘特征提取的BEMD自适应算法[J]. 哈尔滨工业大学学报, 2013, 45(2):117-122. Liu B, Lin Y, Wang Y L. Bi-dimensional empirical mode decomposition algorithm for underwater image edge detecting[J]. Journal of Harbin Institute of Technology, 2013, 45(2):117-122. (in Chinese) [17] 郭雷. Marr-Hildreth边界检测器定位性能分析[J]. 自动化学报, 1990, 16(1):40-45. Guo L. Marr-Hildreth edge detector location error[J]. Acta Automatica Sinica, 1990, 16(1):40-45. (in Chinese) [18] Trottier L, Giguere P, Chaib-Draa B. Parametric exponential linear unit for deep convolutional neural networks[C]//201716th IEEE International Conference on Machine Learning and Applications, 2017:207-214. [19] Hendrycks D, Gimpel K. Gaussian error linear units (GELUs)[DB/OL]. (2020-07-08)[2022-10-15]. https://arxiv.org/abs/1606.08415. [20] Kennedy J, Eberhart R. Particle swarm optimization[C]//Proceedings of ICNN'95-International Conference on Neural Networks, IEEE, 1995, 4:1942-1948. [21] Cutter G, Stierhoff K, Zeng J. Automated detection of rockfish in unconstrained underwater videos using Haar cascades and a new image dataset:labeled fishes in the wild[C]//2015 IEEE Winter Applications and Computer Vision Workshops, 2015:57-62. [22] Li J, Skinner K A, Eustice R M, et al. WaterGAN:unsupervised generative network to enable real-time color correction of monocular underwater images[J]. IEEE Robotics and Automation Letters, 2017, 3(1):387-394. [23] Duarte A, Codevilla F, Gaya J D O, et al. A dataset to evaluate underwater image restoration methods[C]//OCEANS 2016-Shanghai, IEEE, 2016:1-6. [24] Jian M, Qi Q, Dong J, et al. The OUC-vision large-scale underwater image database[C]//2017 IEEE International Conference on Multimedia and Expo, 2017:1297-1302. [25] Underwater photography fish database[DB/OL]. (2018-01-01)[2022-10-15]. http://www. fishdb.co.uk/. [26] Northeast Fisheries Science Center. Habitat mapping camera (HAB-CAM)[DB/OL]. (2012-06-01)[2022-10-15]. https://inport.nmfs.noaa.gov/inport/item/27598. [27] Liu R S, Fan X, Zhu M, et al. Real-world underwater enhancement:challenges, benchmarks, and solutions under natural light[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2020, 30(12):4861-4875. [28] Marques T P, Albu A B, Hoeberechts M. A contrast-guided approach for the enhancement of low-lighting underwater images[J]. Journal of Imaging, 2019, 5(10):79-83. [29] Berman D, Levy D, Avidan S, et al. Underwater single image color restoration using hazelines and a new quantitative dataset[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020, 43(8):2822-2837. |