基于深渊鱼类识别的原位自主观测方法

doi:10.13229/j.cnki.jdxbgxb20180160

Abstract

Abstract:

In?situ observation of hadal fish has been widely implemented for scientific research, usually on serial time or fixed time interval observation mode. However, the observation efficiency is extremely low since pre?programmed method cannot perceive the interested targets in camera view. A novel autonomous observation method combined with computer vision technology is proposed, where observation strategy could be dynamically adjusted according to the result of fish recognition. Moving targets are rapidly segmented from video frames based on improved background difference method. Invariant moment, eccentricity and roundness characteristics are extracted subsequently, and Fisher discriminant function is used for feature reduction. Fish target prediction model is then established with PSO?SVM algorism. The effectiveness of proposed autonomous observation method is validated through simulation experiment using in?situ observation video data of hadal trench expedition.

Key words: marine engineering and technology, autonomous observation, support vector machine, fish recognition, hadal fauna, video camera system

CLC Number:

TP274

Jun CHEN,Qi⁃feng ZHANG,Ai⁃qun ZHANG,Du⁃si CAI. In⁃situ autonomous observation method based onhadal fish recognition[J].Journal of Jilin University(Engineering and Technology Edition), 2019, 49(3): 953-962.

Figures/Tables 15

Fig.1

Fig.2

Fig. 3

Fig. 4

Fig. 5

Fig.6

Table 1

Table 2

Normalized characteristic attribute of typical hadal fauna"

特征物种	$? 1$	$? 2$	$? 3$	$? 4$	$? 5$	$? 6$	$? 7$	$E$	$R c$
鱼类	0.4064	0.2566	0.0630	0.0546	0.0209	0.1080	0.3845	0.5339	0.1624
端足类	0.1424	0.0676	0.0017	0.0004	0.0178	0.0783	0.3838	0.2597	0.6076
等足类	0.6561	0.5294	0.0949	0.0840	0.0252	0.1431	0.3824	0.8323	0.2040
十足类	0.6544	0.5164	0.3750	0.3437	0.1389	0.3397	0.3625	0.7535	0.1749
多毛类	0.2381	0.1306	0.0030	0.0010	0.0178	0.0784	0.3838	0.3935	0.3542
水母	0.0460	0.0087	0.0374	0.0010	0.0412	0.0717	0.2688	0.0636	0.3674

Table 2

Fig. 7

Table 3

Recognition rate of different feature combinations"

特征向量	平均识别准确率/%
$? 1, …, ? 7, E, R c$	89.31
$? 1, …, ? 6, E, R c$	89.26
$? 1, ? 2, ? 3, ? 4, ? 6, E, R c$	89.41
$? 1, ? 2, ? 4, ? 6, E, R c$	88.33

Table 3

Table 4

Fig. 8

Fig. 9

Fig.10

Table 5

References 24

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场景	算法	时间/ms
无运动目标	GMM	635
无运动目标	本文算法	74
少量运动目标	GMM	628
少量运动目标	本文算法	95
较多运动目标	GMM	641
较多运动目标	本文算法	212

组号	T_P	F_N	T_N	F_P	R/%	P/%	A/%
1	244	54	118	3	81.9	98.8	86.4
2	97	28	8	0	77.6	100	78.9
3	196	94	170	18	67.6	91.6	76.6

	自主观测		连续观测		固定间隔观测
	TY?12	TY?14	TY?12	TY?14	TY?12	TY?14
T_o/h	6.66	3.54	6.66	3.54	6.66	3.54
T_rec/h	4.49	3.03	13.97	13.77	2.8	2.77
T_f/h	4.01	2.14	6.66	3.54	1.33	0.66
η_i/%	60.21	60.45	100	100	19.97	18.64
η_e/%	89.31	70.63	47.67	25.71	47.5	23.83
η/%	53.77	42.70	47.67	25.71	9.49	4.44

In⁃situ autonomous observation method based onhadal fish recognition

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