Page 33 - 《应用声学》2021年第4期
P. 33

第 40 卷 第 4 期    逄岩等: 基于 Gammatone 滤波器组时频谱和卷积神经网络的海底底质分类                                     517


                                                                   ral networks[J]. Journal of Chinese Mini-Micro Computer
             4 结论                                                  Systems, 2019, 40(9): 1825–1831.
                                                                 [9] Parkhi O M, Vedaldi A, Zisserman A. Deep face recogni-
                 本文将深度学习的思想应用到海底底质分类                               tion[C]// British Machine Vision Conference, 2015.
             当中,将底质图像信号的 Gammatone 滤波器组时                        [10] 闫琰. 基于深度学习的文本表示与分类方法研究 [D]. 北京:
                                                                   北京科技大学, 2016.
             频谱作为 CNN 模型的输入,进行底质的分类识别,                          [11] Berthold T, Leichter A, Rosenhahn B, et al. Seabed sedi-
             取得了较高的分类准确率,分类准确率均优于其他                                ment classification of side-scan sonar data using convolu-
                                                                   tional neural networks[C]// 2017 IEEE Symposium Series
             常用底质分类的方法。同时,本文进一步验证了所
                                                                   on Computational Intelligence (SSCI). IEEE, 2017.
             提出的方法对于泥底质具有很好的泛化能力,但对                             [12] Luo X, Qin X, Wu Z, et al. Sediment classification of
             于沙底质和泥底质,泛化能力不强,需要通过增加                                small-size seabed acoustic images using convolutional neu-
                                                                   ral networks[J]. IEEE Access, 2019, PP(99): 1.
             CNN 模型层数或者增加沙和石底质训练样本数量
                                                                [13] Darling A M. Properties and implementation of the gam-
             来进一步完善本文提出的方法。此外,本文采用的                                matone filter: a tutorial[J]. Speech Hearing and Language,
             底质信号数据集均提取于底质的侧扫声呐图像,未                                Work in Progress, 1991: 43–61.
                                                                [14] 钱思冲, 向阳, 肖小勇, 等. 基于 Gammatone 滤波器组的内燃
             来有待进一步使用原始的底质侧扫数据进行研究
                                                                   机气缸盖振动特性研究 [J]. 内燃机工程, 2013, 34(6): 36–42.
             论证,以实现在实际中的应用。                                        Qian Sichong, Xiang Yang, Xiao Xiaoyong, et al. Applica-
                                                                   tion of Gammatone filter bank to vibration characteristics
                                                                   analysis of engine cylinder head[J]. Chinese Internal Com-
                            参 考     文   献
                                                                   bustion Engine Engineering, 2013, 34(6): 36–42.
                                                                [15] 刘海燕, 田钢, 石战结. 几种时频分析方法的比较和实际应
              [1] 郑红霞, 张训华. 海底底质分类方法综述 [C]// 中国地球物理                用 [J]. CT 理论与应用研究, 2015(2): 199–208.
                 2013——第二十八专题论文集, 2013.                            Liu Haiyan, Tian Gang, Shi Zhanjie. The comparison
              [2] Blondel P, Sichi O G. Textural analyses of multibeam  of time-frequency analysis methods and their applica-
                 sonar imagery from Stanton Banks, Northern Ireland  tions[J]. Computerized Tomography Theory and Appli-
                 continental shelf[J]. Applied Acoustics, 2008, 70(10):  cations, 2015(2): 199–208.
                 1288–1297.                                     [16] 董建华, 顾汉明, 张星. 几种时频分析方法的比较及应用 [J].
              [3] 杨词银, 许枫. 基于二次反锐化掩模的多特征侧扫声纳成像海                    工程地球物理学报, 2007, 4(4): 312–316.
                 底底质分类 [J]. 电子学报, 2005, 33(10): 1841–1844.         Dong Jianhua, Gu Hanming, Zhang Xing. A compar-
                 Yang Ciyin, Xu Feng. Multi-feature seafloor sediments  ison of time-frequency analysis methods and their ap-
                 classification for sidescan sonar imagery based on a  plications[J]. Chinese Journal of Engineering Geophysics,
                 quadratic unsharp masking operator[J]. Chinese Journal  2015(2): 199–208.
                 of Electronics, 2005, 33(10): 1841–1844.       [17] 常亮, 邓小明, 周明全, 等. 图像理解中的卷积神经网络 [J]. 自
              [4] Pace N G, Gao H. Swathe seabed classification[J]. IEEE  动化学报, 2016, 42(9): 1300–1312.
                 Journal of Oceanic Engineering, 1988, 13(2): 83–90.  Chang Liang, Deng Xiaoming, Zhou Mingquan, et al.
              [5] Tamsett D. Sea-bed characterisation and classification  Convolutional neural networks in image understanding[J].
                 from the power spectra of side-scan sonar data[J]. Marine  Acta Automatica Sinica, 2016, 42(9): 1300–1312
                 Geophysical Researches, 1993, 15(1): 43–64.    [18] 倪志伟. BP 网络中激活函数的深入研究 [J]. 安徽大学学报
              [6] Chu W, Champagne B. A simplified early auditory model  (自然科学版), 1997(3): 48–51.
                 with application in speech/music classification[C]// Con-  Ni Zhiwei. Deep study on activation function in BP net-
                 ference on Electrical & Computer Engineering.  IEEE,  work[J]. Journal of Anhui University(Natural Science Edi-
                 2007.                                             tion), 1997(3): 48–51.
              [7] 李允公, 张金萍, 戴丽, 等. 基于听觉模型 ZCPA 的故障              [19] Lecun Y, Bottou L. Gradient-based learning applied to
                 诊断特征提取方法研究 [J]. 中国机械工程, 2009, 20(24):             document recognition[J]. Proceedings of the IEEE, 1998,
                 2988–2992.                                        86(11): 2278–2324.
                 Li Yungong, Zhang Jinping, Dai Li, et al. Study on the  [20] van Walree P A, Tęgowski J, Laban C, et al. Acous-
                 ZCPA-auditory-model-based method of feature extraction  tic seafloor discrimination with echo shape parameters: a
                 for mechanical faults diagnosis[J]. China Mechanical En-  comparison with the ground truth[J]. Continental Shelf
                 gineering, 2009, 20(24): 2988–2992.               Research, 2005, 25(18): 2273–2293.
              [8] 张泽苗, 霍欢, 赵逢禹. 深层卷积神经网络的目标检测算法综                [21] Atallah L, Smith P J P. Using wavelet analysis to classify
                 述 [J]. 小型微型计算机系统, 2019, 40(9): 1825–1831.         and segment sonar signals scattered from underwater sea
                 Zhang Zemiao, Huo Huan, Zhao Fengyu. Survey of ob-  beds[J]. International Journal of Remote Sensing, 2003,
                 ject detection algorithm based on deep convolutional neu-  24(21): 4113–4128.
   28   29   30   31   32   33   34   35   36   37   38