Acoustic Identification of Six Fish Species using an Artificial Neural Network

224

Copyright © 2016 The Korean Society of Fisheries and Aquatic Science pISSN:0374-8111, eISSN:2287-8815

서 론

어로현장에서어획대상어류의어종이나체장등과같은생 물학적정보를알수있다면

,

.

,

.

,

(Woillez et al., 2012)

echo trace

(Fernandes, 2009)

, echogram

(Tasgarakis et al., 2015),

광대역

echo

(simmonds

et al., 1996; Lee et al., 2015; Lee, 2015a; Lee, 2015b)

한연구가있다

.

,

.

,

(Lee et al., 2015; Lee, 2015a;

Lee, 2015b).

chirp

-

echo

(set)

,

(artificial neural network, ANN)

,

.

,

(accuracy)

,

,

(activation function),

인공 신경망에 의한 6개 어종의 음향학적 식별

이대재*

부경대학교 해양생산시스템관리학부

Acoustic Identification of Six Fish Species using an Artificial Neural Network

Dae-Jae Lee*

Division of Marine Production System Management, Pukyong National University, Busan 48513, Korea

The objective of this study was to develop an artificial neural network (ANN) model for the acoustic identification of commercially important fish species in Korea. A broadband echo acquisition and processing system operating over the frequency range of 85-225 kHz was used to collect and process species-specific, time-frequency feature images from six fish species: black rockfish Sebastes schlegeli , black scraper Thamnaconus modesutus [K], chub mackerel Scomber japonicus , goldeye rockfish Sebastes thompsoni , konoshiro gizzard shad Konosirus punctatus and large yellow croaker Larimichthys crocea . An ANN classifier was developed to identify fish species acoustically on the basis of only 100 dimension time-frequency features extracted by the principal components analysis (PCA). The overall mean identification rate for the six fish species was 88.5%, with individual identification rates of 76.6% for black rockfish, 82.8% for black scraper, 93.8% for chub mackerel, 90.6% for goldeye rockfish, 96.9% for konoshiro gizzard shad and 90.6% for large yellow croaker, respectively. These results demonstrate that individual live fish in well-controlled environments can be identified accurately by the proposed ANN model.

Key words: Fish species identification, Time-frequency image, Artificial neural network, Principal components anal- ysis, Confusion matrix

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial Licens (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

http://dx.doi.org/10.5657/KFAS.2016.0224 Korean J Fish Aquat Sci 49(2) 224-233, April 2016 Received 18 March 2016; Accepted 29 March 2016

*Corresponding author: Tel: +82. 51. 629. 5889 Fax: +82. 51. 629. 5885

E-mail address: [email protected]

적절성

,

(weight)

.

,

.

,

,

,

(Lee et al., 2016).

이때문에이미지패턴을해석하는데있어서는데이터에내포 된주성분의많은축들이

3

(principal components analysis, PCA)

(Zuo et al., 2006; Santo, 2012).

chirp

3

-

echo

(Imberger and Boashash, 1986; Dong and Cui, 2012; Sui et al., 2007)

35×75

echo

.

터를그대로인공신경망의입력층뉴런에연결하면

2,625

의입력데이터가되기때문에학습과정에서매우많은시간이 소요되어비효율적이다

.

,

-

echo

이미지의데이터속성을나타내는

PCA

matrix

고

,

.

본연구에서는이와같은점에주목하여조피볼락

,

,

,

,

,

6

16

96

85-225 kHz

에대한시간

-

echo

,

이터의차원을저감시킨후

, PCA matrix

음향학적식별성능의변화를인공신경망모델을구축하여분 석

,

.

재료 및 방법

어류에 의한 시간-주파수 echo 이미지의 데이터 차원 축소

본연구에서는우리나라주변수역에서어획되는조피볼락

,

,

,

,

,

6

85-225 kHz

16

96

리의개체어에 대한 시간

-

echo

(Lee et al., 2016).

,

chirp echo

(dorsal aspect)

±25°

2.5°

,

320

(16

×20

/

),

, 6

1,920

의시간

-

echo

한데이터베이스를구축하였다

.

6

chirp echo

SPWVD (smoothed pseudo-Wigner-Ville distribution)

간

-

,

0-0.3 ms

85-225 kHz

i (column)×j (row)

(pixel)

(N = i×j )

(cutting)

.

-

I

,

I

= [ ^{p p ⋮ p}

^{p p ⋮ p}

^{… … … ⋮ p p p}

] ⁽¹⁾

이되고

,

column

F

F

= {p

p

… p

p

p

… p

… p

p

… p

} (2)

.

-

M

,

S

S

= [F

, F

, F

… F

]

(3)

이된다

.

, [*]

[*]

(transposed matrix)

.

,

S

PCA

,

W

,

W = [ ^{w w ⋮ w}

^{w w w ⋮}

^{… … … ⋮ w w w}

] ⁽⁴⁾

이 된다

.

,

.

,

K

(W

)

W

= [ ^{w w ⋮ w}

^{w w w ⋮}

^{… … … ⋮ w w w}

] ⁽⁵⁾

이된다

.

본연구에서는이와같이

K

PCA

W

N

-

K

-

.

,

S

S

= W

· S

(6)

에의해구하였다

.

다중 퍼셉트론 인공 신경망과 어류의 시간-주파수 이미지 데이터의 학습

일반적으로인공신경망모델에서는뉴런

(neuron,

node)

,

.

,

(multilayer perceptron network, MLP)

,

MLP

Fig. 1

. Fig. 1

j

i

x

,

(connection weight)

w

,

j

(bias)

b

, Fig. 1

MLP

.

,

(activation function)

(7)

(Saad et al., 2007: Bai et al., 2009; Latha el al., 2009).

y

= f (net

) = f

∑

w

· x

+ b

) (7)

j=1

여기서

, net

j

가중치와임계치를반영한가중합

,

,

. f

, y

j

net

,

.

, Fig. 1

k

j

y

,

의연결가중치를

w

,

j

(bias) b

net

,

f ( net

)

z

.

z

= f (net

) = f

h

(8)

∑

w

· y

+ b

)

j=1

이와같은과정을거쳐산출되는인공신경망의실제의출력

z

t

,

, (9)

(error threshold)

.

E =

1

‖t - z‖

(9)

{

∑

(t

- z

)

=

즉

, Fig. 1

(9)

E

최소로 하는연결 가중치벡터가산출된다

(Bai et al., 2009;

Latha el al., 2009).

MLP

(error backpropagation, BP)

(supervised learning)

,

,

.

,

∆w

∆b

,

w (old)

b (old)

w (new)

b (new)

(10)

(11)

(Latha el al., 2009;

Pinjare and Arun Kumar, 2012).

w (new) = w (old) + ∆w (10)

b (new) = b (old) + ∆b (11)

본 연구에서 인공 신경망의 지도학습은

Matlab

(tool)

newff (feed-forward backpropagation network)

(Demuth et al., 2009),

, Fig. 1

BP

기위한학습은

traingdx

(gradient descent with variable learning rate and momentum)

.

,

(9)

(performance function) E

,

(mean square error, MSE)

(Kuruvilla and Gunavathi, 2014).

traingdx

Table1

(Shilbayeh et al., 2013).

Table 1

matlab

newff

net

, train

net

net.trainFcn

net.trainParam

net

,

(return)

net

work with one hidden layer and basic block of error back-prop- agation process. The architecture represents a three-layered MLP with h neurons in the hidden layer and d neurons in the input layer corresponding to the time-frequency echo patterns of 6 fish spe- cies. The m nodes in the output layer indicate the m different fish species to be predicted.

로망을

sim

.

,

(activation)

hyperbolic tangent

,

tan-sigmoid

-1

+1

(Bai et al., 2009).

결과 및 고찰

어종별 학습용 및 테스트용 이미지의 특징적인 패턴

6

, 96

한시간

-

echo

그룹으로분류하였다

.

,

.

,

베이스는각어종별로수록한

320

-

echo

중에서무작위로

80%(256

)

.

한

,

20%

,

Fig. 2

. Fig. 2

(a)

(b)

chirp echo

Fig. 2. Original train image sets for black rockfish Sebastes schlegeli consisting of 320 color-scale images each with a matrix dimension of 35×75 pixels in the frequency and time domains. These SPWVD images were obtained from the broadband echoes by 16 live individuals over the frequency range of 85 to 225 kHz. (a) The 256 train images, for the supervised learning of ANN, corresponding to 80% of 320 im- ages were selected by random process. (b) The 64 test images corresponding to 20% of 320 images were used as an image set for identifying the target fish species.

Table 1. The function “traingdx” parameters Training parameters Description net.performFcn = mse Mean square error net.trainParam.goal = 0.001 Performance goal net.trainParam.show = 20 Epochs between displays net.trainParam.epochs = 5000 Maximum number of

epochs to train net.trainParam.mc = 0.95 Momentum constant

신호에대한모든시간

-

echo

RGB

.

35×75

gray

. Fig. 2(a)

, Fig.

2(b)

.

(contour),

,

셀의진폭변동

, echo

, echo

현과소멸구간등의차이등에주목하여분석을행하였다

.

-

,

,

.

,

기를갖는다양한형상의

echo contour

였다

.

contour

6

.

,

contour

feature

.

따라서

,

-

구를수행하였다

.

Fig. 2

SPWVD

320

35×75

2

feature

(reshaping)

Fig.

3

(a)

(b)

. Fig. 3(a)

Fig. 2(a)

35

column

1

번부터

35

2,625

이미지를생성한후

,

256

.

, Fig. 3(b)

Fig. 2(b)

의테스트용이미지셋을대상으로

Fig. 3(a)

형처리를수행하여얻은

64

. Fig. 3

(a)

(b)

에서종축은이미지데이터의번호이고

,

1-2,625

파수축의

35

column (75

/column)

순서대로연결하여생성한

2,625

.

feature

,

,

적성질등을나타내는어종의존적인정보들이내포되어있다

(Gavrovska et al., 2010; Han and Kim, 2010).

,

Fig. 3. (a) A reshaped image set of 256 train images for the supervised learning of ANN (Fig. 2a). (b) A reshaped image set of 64 test images for identifying the target fish species (Fig. 2b). Each matrix for train and test images of black rockfish Sebastes schlegeli was reshaped as a set of 2,625-dimensional feature pixels by concatenating the 35 columns of the 35×75 image matrix.

어종의식별성능을결정하는중요한요소가된다

. Fig. 3

(a)

(b)

16

트용이미지패턴을살펴볼때

,

1,000

의이미지영역에서는매우불규칙한진폭변동이관찰되었지

만

, 1,000-2,300

어종고유의공통적인변동패턴이나타나고있음을알수있었

다

.

, 2,300

소멸해가는경향을나타내었다

.

,

.

, Fig. 3

(a)

(b)

바와같이각개체어의이미지는

2,625

패턴이고

,

256

,

256×2,625

.

,

.

,

PCA

6

.

, Fig.

3(a)

PCA

수행렬로부터

(5)

PCA

W

.

,

Fig. 3(b)

.

,

feature space

,

종의존적인정보의손실이초래되어어종식별율이저하하게 되고

,

,

feature space

,

,

.

,

,

.

시간-주파수 이미지 데이터의 차원 축소에 따른 어종 식별율의 변화

시간-주파수 이미지 데이터의 차원 축소

본연구에서는

6

256

-

,

.

, 1

-

echo

은

75×35

층의뉴런에접속하려면총

2,625

.

,

.

,

,

,

원수를감소시킬필요가있다

.

PCA

해얻어진입력이미지데이터의

PCA

에의한어종식별율에미치는영향을분석

,

,

Fig. 4

. Fig. 4

16

320

-

echo

Fig. 4. A lower dimensional representation of the reduced feature matrix for the time-frequency echo pattern of black rockfish Sebastes schlegeli as a function of the number of principal components (eigenvectors). By only considering the first 250 eigenvectors of 2,526 eigen- vectors, the dimensionality of the image feature space (matrix) was greatly reduced. The supervised learning of ANN for identifying the fish species was accomplished by only using a truncated set of the first 100 eigenvectors.