Review on Key Technologies of Target Exploration in Underwater Optical Images

Sen Lin; Ying Zhao

doi:10.3788/LOP57.060002

Journals >Laser & Optoelectronics Progress >Volume 57 >Issue 6 >Page 060002 > Article

Laser & Optoelectronics Progress
Vol. 57, Issue 6, 060002 (2020)

Review on Key Technologies of Target Exploration in Underwater Optical Images

Sen Lin^1,3,4 and Ying Zhao^1,2,*

Author Affiliations

¹School of Electronic and Information Engineering, Liaoning Technical University, Huludao, Liaoning 125105, China

²Institute of Graduate, Liaoning Technical University, Huludao, Liaoning 125105, China

³State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, Liaoning 110016, China

⁴Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, Liaoning 110016, China

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DOI: 10.3788/LOP57.060002 Cite this Article Set citation alerts

Sen Lin, Ying Zhao. Review on Key Technologies of Target Exploration in Underwater Optical Images[J]. Laser & Optoelectronics Progress, 2020, 57(6): 060002 Copy Citation Text

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Fig. 1. Traditional methods of underwater image feature extraction

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Method	Principle	Characteristic	Limitation
Histogram-based	Improving image contrast throughnonlinear stretching of pixel values	Bright color,real-time	Lack of texture details,increasing noise
Retinex-based	Removing the influence of illuminatinglight from the image to obtain thereflective properties of the object	Bright color,high contrast,more detail information	Over-enhancement for thebrighter patch, unsaturationfor darker region
Fusion-based	Combining relevant informationfrom two or more images intoa single image, which is moreinformative than any of the inputs	Effective colorcorrection,high contrast	Less robust, affected byartificial illumination
Deep learning-based	Imitating the working of thehuman brain in processing data,and improving the quality of underwaterimage through network training	Effective colorcorrection, robustcontrast stretching	Hard to get ground-truthimages, time-consumingfor training

Table 1. Comparison of underwater image enhancement methods

Method	Improvement	Characteristic
DCP-based	Tang et al.^[25]:the depth-of-field map is obtained bydisparity between bright and dark channels, so as toestimate the background color of water body andtransmittance map more accurately	High real-time performance,more authentic colors,influence of artificial lightsources, and inadequaterobustness and adaptability
		Xu et al.^[26]:estimation oftransmittance map of images byred channel algorithms
		Xie et al.^[27]:using the relationship betweenthe wavelength of visible light and the scatteringcoefficient to obtain transmittance maps of each color channel
Deeplearning-based^[23]	URCNN: a convolutional layer and ReLU are used to generatefeature maps, then the batch normalization is addedbetween convolutional layer and ReLU to speed up training process.This pattern is repeated until the transmission map is output	More authentic colors,robustness, and beingtime-consuming for training
		UIRNet:the transmission map and the backgroundlight are estimated and computedby BL-Net and TM-Net, respectively
		WaterGAN^[24]:taking in-air images anddepth maps as input and generatingcorresponding synthetic underwater images as output

Table 2. Comparison of underwater scene coefficient estimation methods

Author	Method	Advantage and limitation
Salmanet al.^[43]	A hybrid approach involving GMM,optical flow, and deep R-CNN tofine-tune the categorization of fish	Higher classification accuracy;requirement on relatively morecomputational resources
Siddiquiet al.^[44]	A cross-layer pooling algorithm usinga pre-trained convolutional neuralnetwork as a generalized feature detector	No need for a large amount of training data;high classification accuracy;requirement of extensive computations
Sunet al.^[45]	A CNN knowledge transfer framework forunderwater object recognition andextracting discriminative featuresfrom relatively low contrast images	High real-time performance;no need for a large amount of training data;lower robustness
Chuanget al.^[46]	An underwater fish recognition frameworkthat consists of a fully unsupervisedfeature learning technique and anerror-resilient classifier	Successfully handled data uncertaintyand class imbalance in practicalclassification applications;lower robustness
Caoet al.^[42]	A method to combine CNN andhand-designed features toimprove classification performance	Being robust, reducing featuredimensionality without decreasingclassification performance;contour extraction needing original video frames

Table 3. Comparison of depth neural network for underwater target detection and recognition

Method	Improvement	Principle	Characteristic
Optical flow-based	Modified Lucas-Kanade opticalflow method based on pyramidhierarchy and affine transformation	Estimating the positionby calculating thevelocity in twoconsecutive frames	Need of a largenumber ofprecise imagefeature points
Mean shift-based	1) Adaptive mean shift algorithmthrough color histogram based onbackground and target region;2) adaptive mean shift algorithmcombined with edge information;3) combined with color, texture,HOG features, and deformablemulti-core algorithm	Using the colorhistogram of the targetas the searchfeature, and iteratingthe mean shiftvector continuously	Being robust torange variations;being influencedby multi-objects
CNT-based	1) Fast-CNT: improving thecomputing performance byselecting adaptive K value andomitting background filter;2) improved Fast-CNT:extracting each region containinga moving target through Gaussianmixture model	Tracking target throughtwo-layer forwardconvolution networkslearning image features	No need for off-linetraining which istime-consumingand requires lotsof training data

Table 4. Comparison of underwater target tracking methods

Sen Lin, Ying Zhao. Review on Key Technologies of Target Exploration in Underwater Optical Images[J]. Laser & Optoelectronics Progress, 2020, 57(6): 060002

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