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A Literature Review of Underwater Image Detection [chapter]

Yawen Wang, Qinxiao Wang, Shasha Jin, Wei Long, Lingxi Hu
2022 Frontiers in Artificial Intelligence and Applications  
In this paper, first of all introduces the existing research of underwater target image recognition, mainly presents the underwater image recognition technology based on deep learning.  ...  As a specific application of computer recognition technology, underwater image recognition technology is important to promote aquaculture industry automation and intelligence.  ...  Prospect The recognition and detection of underwater images are the prerequisites for the application of computer technology to aquaculture playing an important role in the development of fishing, precision  ... 
doi:10.3233/faia220009 fatcat:ynod4ci7tvaitnwvgcmljfxix4

ROV-based Underwater Vision System for Intelligent Fish Ethology Research

Rui Nian, Bo He, Jia Yu, Zhenmin Bao, Yangfan Wang
2013 International Journal of Advanced Robotic Systems  
The underwater vision system can further detect and track swimming fish from the resulting images with the strategies developed using curve evolution and particular filtering, in order to obtain a deeper  ...  Fish ethology is a prospective discipline for ocean surveys. In this paper, one ROV-based system is established to perform underwater visual tasks with customized optical sensors installed.  ...  This work was fully supported by the Natural Science Foundation of P. R. China (31202036), the National High-Tech R&D 863 Program (2012AA10A412), and the Natural Science Foundation of P. R.  ... 
doi:10.5772/56800 fatcat:jpgyr5pfhzdn5n2nrghywz2v7a

Computer Vision and Deep Learning for Fish Classification in Underwater Habitats: A Survey [article]

Alzayat Saleh, Marcus Sheaves, Mostafa Rahimi Azghadi
2022 arXiv   pre-print
In this paper, we provide an overview of the key concepts of DL, while presenting a survey of literature on fish habitat monitoring with a focus on underwater fish classification.  ...  This paper aims to inform a wide range of readers from marine scientists who would like to apply DL in their research to computer scientists who would like to survey state-of-the-art DL-based underwater  ...  The labelled data is the set of data that needs the labelling of classes, e.g. fish species in an image, or absence or presence of fish in an image.  ... 
arXiv:2203.06951v2 fatcat:oacfgeo5evanpatgnlq2hhne4y

Recent advances of machine vision technology in fish classification

Daoliang Li, Qi Wang, Xin Li, Meilin Niu, He Wang, Chunhong Liu, Cigdem Beyan
2022 ICES Journal of Marine Science  
This paper provides an overview of machine vision models applied in the field of fish classification, followed by a detailed discussion of specific applications of various classification methods.  ...  This paper would help researchers and practitioners to understand the applicability of machine vision in fish classification and encourage them to develop advanced algorithms and models to address the  ...  To overcome the complexity of the underwater environment, Fabic et al. (2013) chose to preprocess the underwater images that are, extract the edge contours of the fish before using shape analysis based  ... 
doi:10.1093/icesjms/fsab264 fatcat:3keyhjom2fhb7fuwayffowww6i

Underwater Fish Species Classification using Convolutional Neural Network and Deep Learning [article]

Dhruv Rathi, Sushant Jain, Dr. S. Indu
2018 arXiv   pre-print
This method uses a novel technique based on Convolutional Neural Networks, Deep Learning and Image Processing to achieve an accuracy of 96.29%.  ...  Majority of available methods focus on classification of fishes outside of water because underwater classification poses challenges such as background noises, distortion of images, the presence of other  ...  [15] worked on 20 underwater videos to detect, track and count fishes with an accuracy of 85%.  ... 
arXiv:1805.10106v1 fatcat:b7sbxjj5pfho5ftuqodyhy3eq4

Underwater Optical Fish Classification System by Means of Robust Feature Decomposition and Analysis using Multiple Neural Networks

Mohcine Boudhane, Benayad Nsiri, Taoufiq Belhoussine
2018 International Journal of Advanced Computer Science and Applications  
Optical underwater imaging systems can also have detection problems such as changing appearance/orientation of objects, and changes in the scene.  ...  In this paper, we propose a new object classification system for underwater optical images. The proposed method is based on robust feature extraction from fish pattern.  ...  Our goal was to develop a system that detects and recognizes an array of different types of fish in images and videos, including various structures in underwater images.  ... 
doi:10.14569/ijacsa.2018.091286 fatcat:vawptj4jkfhb3p2e6ixweb4rty

Factors Affecting the Training of a WISARD Classifier forMonitoring Fish Underwater

D. Chan, S. Hockaday, R.D. Tillett, L.G. Ross
1999 Procedings of the British Machine Vision Conference 1999  
Experimental results show that the classifier is a useful tool for interpreting underwater fish images and could be used as a tool to aid the application in question.  ...  The experiments were designed to identify factors that will affect the performance and the usefulness of the classifier under the requirements of the fish inspection application.  ...  Hence, an n-tuple classifier was developed and trained to identify this distinctive fish head shape.  ... 
doi:10.5244/c.13.34 dblp:conf/bmvc/ChanHTR99 fatcat:pocaxs24krc4ddxt4vcbdeodme

A Survey of Target Detection and Recognition Methods in Underwater Turbid Areas

Xin Yuan, Linxu Guo, Citong Luo, Xiaoteng Zhou, Changli Yu
2022 Applied Sciences  
Based on analysis of state-of-the-art research investigating target detection and recognition in turbid waters, and aiming to solve the problems encountered during target detection and the unique influences  ...  This work provides a reference for engineering tasks in underwater turbid areas and an outlook on the development of underwater intelligent sensing technology in the future.  ...  Percentages of different application areas. Figure 2 . 2 Figure 2. Underwater images of turbid water. (a) An underwater photo of a fish farm. (b) An underwater image of shallow water.  ... 
doi:10.3390/app12104898 fatcat:ytmjcsquhjbs5fpqllbsl3vrzq

Underwater Fish Detection with Weak Multi-Domain Supervision

Dmitry A. Konovalov, Alzayat Saleh, Michael Bradley, Mangalam Sankupellay, Simone Marini, Marcus Sheaves
2019 2019 International Joint Conference on Neural Networks (IJCNN)  
In this work we present a labelling-efficient method of training a CNN-based fish-detector (the Xception CNN was used as the base) on relatively small numbers (4,000) of project-domain underwater fish/  ...  However, for the task of fish classification and/or fish detection, if a CNN was trained to detect or classify particular fish species in particular background habitats, the same CNN exhibits much lower  ...  Fish Localization Fish detection normally implies localization of the detected fish within an image.  ... 
doi:10.1109/ijcnn.2019.8851907 dblp:conf/ijcnn/KonovalovSBSMS19 fatcat:cgcrqrtuwzde5osnu6wbcnizj4

Automated fish cage net inspection using image processing techniques

Konstantia Moirogiorgou, Stavros Paspalakis, Michalis Zervakis, Nikos Papandroulakis, George Giakos
2020 IET Image Processing  
In this study, we explore specialised image processing schemes to detect net holes of multiple area size and shape.  ...  State-of-the-art According to the FAO handbook of aquaculture operations in floating HDPE cages [4], the default way to monitor underwater  ...  To alleviate the false detection of holes due to a set threshold on the histogram, which might oversee low-end irregularities in the histogram distribution, we tested an alternative threshold based on  ... 
doi:10.1049/iet-ipr.2019.1667 fatcat:66bx7bhkfbavhfz4lip7u3ytbe

A novel method to detect foreground region using morphological operations with block based enhancement for underwater images

M Sudhakar, M Janaki Meena
2018 International Journal of Engineering & Technology  
Automation of detecting the Foreground Region (FR) or Shape of the object is essential in several computer vision, object recognition applications and poses several challenges in case of underwater images  ...  We have decomposed the image in to multiple levels based on discrete wavelet transforms (DWT) for improving the sharpness or to reduce the fogginess in the image in order to get the clear image.  ...  The research may be extended to identify the accurate shape of the smallest objects in the underwater image using gray level morphological operations.  ... 
doi:10.14419/ijet.v7i3.13159 fatcat:yrgru6aginbc3mytallktl3e3y

Quantitative performance analysis of object detection algorithms on underwater video footage

Isaak Kavasidis, Simone Palazzo
2012 Proceedings of the 1st ACM international workshop on Multimedia analysis for ecological data - MAED '12  
Object detection in underwater unconstrained environments is useful in domains like marine biology and geology, where the scientists need to study fish populations, underwater geological events etc.  ...  In this paper, we evaluated the performance of six state-ofthe-art object detection algorithms in the task of fish detection in unconstrained, underwater video footage, discussing the properties of each  ...  A decision-theoretic generalization of on-line learning and an application to boosting, 1995. Figure 1 : 1 ROC Curves of the performance of the algorithms in object detection.  ... 
doi:10.1145/2390832.2390847 dblp:conf/mm/KavasidisP12 fatcat:hdwjz7mgbzci3jaiwlmez5mzaa

A Review of Computer Vision Technologies for Fish Tracking [article]

Zhenbo Li, Weiran Li, Fei Li, Meng Yuan
2022 arXiv   pre-print
Firstly, we introduced the open source datasets of fish, and summarized the preprocessing technologies of underwater images.  ...  Most of the applications of fish tracking use classic filtering algorithms, which lack in accuracy and efficiency.  ...  The underwater image processing technology can be divided into image enhancement algorithm and image restoration algorithm according to whether it is based on an underwater imaging physical model.  ... 
arXiv:2110.02551v3 fatcat:kr6uqjmj6zgtnaojnuyumdvwva

High-Accuracy Real-Time Fish Detection Based on Self-Build Dataset and RIRD-YOLOv3

Wenkai Wang, Bingwei He, Liwei Zhang, Xin Dong
2021 Complexity  
To better detect fish in an aquaculture environment, a high-accuracy real-time detection model is proposed.  ...  To overcome the inaccuracy of the You Only Look Once v3 (YOLOv3) algorithm in underwater farming environment, a suitable set of hyperparameters was obtained through multiple sets of experiments.  ...  It is able to solve the problem of image blur and noise caused by processing in an underwater environment.  ... 
doi:10.1155/2021/4761670 fatcat:kektepg6ifhpdisbpda2o5vq4q

Assessing fish abundance from underwater video using deep neural networks [article]

Ranju Mandal, Rod M. Connolly, Thomas A. Schlacherz, Bela Stantic
2018 arXiv   pre-print
An end-to-end deep learning-based architecture is introduced which outperformed the state of the art methods and first of its kind on fish assessment task.  ...  Uses of underwater videos to assess diversity and abundance of fish are being rapidly adopted by marine biologists.  ...  Videos are made available through a collaboration between researchers at University of Sunshine Coast and Griffith University.  ... 
arXiv:1807.05838v1 fatcat:dthbvzhocbgzjhlyiq2n3xhcty
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