Relevant bibliographies by topics / Underwater object detection

Academic literature on the topic 'Underwater object detection'

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Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Underwater object detection"

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Shen, Jie, Zhenxin Xu, Zhe Chen, Huibin Wang, and Xiaotao Shi. "Optical Prior-Based Underwater Object Detection with Active Imaging." Complexity 2021 (April 27, 2021): 1–12. http://dx.doi.org/10.1155/2021/6656166.

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Underwater object detection plays an important role in research and practice, as it provides condensed and informative content that represents underwater objects. However, detecting objects from underwater images is challenging because underwater environments significantly degenerate image quality and distort the contrast between the object and background. To address this problem, this paper proposes an optical prior-based underwater object detection approach that takes advantage of optical principles to identify optical collimation over underwater images, providing valuable guidance for extracting object features. Unlike data-driven knowledge, the prior in our method is independent of training samples. The fundamental novelty of our approach lies in the integration of an image prior and the object detection task. This novelty is fundamental to the satisfying performance of our approach in underwater environments, which is demonstrated through comparisons with state-of-the-art object detection methods.
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V, Karthikeyan. "Underwater Object Detection." International Journal for Research in Applied Science and Engineering Technology 8, no. 5 (May 31, 2020): 2091–95. http://dx.doi.org/10.22214/ijraset.2020.5344.

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Maccabee, Bruce S. "Underwater object detection system." Journal of the Acoustical Society of America 91, no. 5 (May 1992): 3081. http://dx.doi.org/10.1121/1.402901.

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Mahavarkar, Avinash, Ritika Kadwadkar, Sneha Maurya, and Smitha Raveendran. "Underwater Object Detection using Tensorflow." ITM Web of Conferences 32 (2020): 03037. http://dx.doi.org/10.1051/itmconf/20203203037.

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Object Detection is a popular technology that detects instances within an image. In order to eliminate the barriers in Computer Vision technology due to the dissolution of the BGR(Blue-Green-Red) constituents with the increase in depth, it has been a necessity that the accuracy and efficiency of detecting any object underwater is optimum. In this article, we conduct Underwater Object Detection using Machine Learning through Tensorflow and Image Processing along with Faster R-CNN (Regions with Convolution Neural Network) as an algorithm for implementation. A suitable environment will be created so that Machine Learning algorithm will be used to train different images of the object. Open source Computer Vision has various functions which can be used for the image processing needs when an image is captured.
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Zhang, Yangmei. "Application of Smart Sensor in Underwater Weak Object Detection and Positioning." Wireless Communications and Mobile Computing 2021 (December 23, 2021): 1–16. http://dx.doi.org/10.1155/2021/5791567.

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This paper is aimed at studying underwater object detection and positioning. Objects are detected and positioned through an underwater scene segmentation-based weak object detection algorithm and underwater positioning technology based on the three-dimensional (3D) omnidirectional magnetic induction smart sensor. The proposed weak object detection involves a predesigned U-shaped network- (U-Net-) architectured image segmentation network, which has been improved before application. The key factor of underwater positioning technology based on 3D omnidirectional magnetic induction is the magnetic induction intensity. The results show that the image-enhanced object detection method improves the accuracy of Yellow Croaker, Goldfish, and Mandarin Fish by 3.2%, 1.5%, and 1.6%, respectively. In terms of sensor positioning technology, under the positioning Signal-to-Noise Ratio (SNR) of 15 dB and 20 dB, the curve trends of actual distance and positioning distance are consistent, while SNR = 10 dB , the two curves deviate greatly. The research conclusions read as follows: an underwater scene segmentation-based weak object detection method is proposed for invalid underwater object samples from poor labeling, which can effectively segment the background from underwater objects, remove the negative impact of invalid samples, and improve the precision of weak object detection. The positioning model based on a 3D coil magnetic induction sensor can obtain more accurate positioning coordinates. The effectiveness of 3D omnidirectional magnetic induction coil underwater positioning technology is verified by simulation experiments.
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ZHANG, Yan, Xingshan LI, Yemei SUN, and Shudong LIU. "Underwater object detection algorithm based on channel attention and feature fusion." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 40, no. 2 (April 2022): 433–41. http://dx.doi.org/10.1051/jnwpu/20224020433.

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Due to the color deviation, low contrast and fuzzy object in underwater optical images, there are some problems in underwater object detection, such as missed detection and false detection. In order to solve the above-mentioned problems, an underwater object detection algorithm is proposed based on the channel attention and feature fusion for underwater optical images. The excitation residual module is designed based on the channel attention, and the forward propagation feature information is adaptively allocated weights to highlight the salience of different channel feature maps, which improves the network ability to extract high-frequency information from the underwater images. The multi-scale feature fusion module is designed to add a large scale feature map for object detection, which improves the detection performance of the network for small size objects by using its corresponding small size receptive field, and further improves the detection accuracy of the network for different size objects in the underwater environment. To improve the generalization performance of the network to the underwater environment, the data augmentation method based on the stitching and fusion is designed to simulate the overlap, occlusion and blurring of underwater objects, which improves the adaptability of the network to the underwater environment. Through experiments on the public dataset URPC, the algorithm in this paper improves the mean average precision by 5.42%, 3.20% and 0.9% compared with YOLOv3, YOLOv4 and YOLOv5, respectively, effectively improving the missed and false detection of objects of different sizes in complex underwater environments.
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Shen, Jie, Tanghuai Fan, Min Tang, Qian Zhang, Zhen Sun, and Fengchen Huang. "A Biological Hierarchical Model Based Underwater Moving Object Detection." Computational and Mathematical Methods in Medicine 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/609801.

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Underwater moving object detection is the key for many underwater computer vision tasks, such as object recognizing, locating, and tracking. Considering the super ability in visual sensing of the underwater habitats, the visual mechanism of aquatic animals is generally regarded as the cue for establishing bionic models which are more adaptive to the underwater environments. However, the low accuracy rate and the absence of the prior knowledge learning limit their adaptation in underwater applications. Aiming to solve the problems originated from the inhomogeneous lumination and the unstable background, the mechanism of the visual information sensing and processing pattern from the eye of frogs are imitated to produce a hierarchical background model for detecting underwater objects. Firstly, the image is segmented into several subblocks. The intensity information is extracted for establishing background model which could roughly identify the object and the background regions. The texture feature of each pixel in the rough object region is further analyzed to generate the object contour precisely. Experimental results demonstrate that the proposed method gives a better performance. Compared to the traditional Gaussian background model, the completeness of the object detection is 97.92% with only 0.94% of the background region that is included in the detection results.
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Karimanzira, Divas, Helge Renkewitz, David Shea, and Jan Albiez. "Object Detection in Sonar Images." Electronics 9, no. 7 (July 21, 2020): 1180. http://dx.doi.org/10.3390/electronics9071180.

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The scope of the project described in this paper is the development of a generalized underwater object detection solution based on Automated Machine Learning (AutoML) principles. Multiple scales, dual priorities, speed, limited data, and class imbalance make object detection a very challenging task. In underwater object detection, further complications come in to play due to acoustic image problems such as non-homogeneous resolution, non-uniform intensity, speckle noise, acoustic shadowing, acoustic reverberation, and multipath problems. Therefore, we focus on finding solutions to the problems along the underwater object detection pipeline. A pipeline for realizing a robust generic object detector will be described and demonstrated on a case study of detection of an underwater docking station in sonar images. The system shows an overall detection and classification performance average precision (AP) score of 0.98392 for a test set of 5000 underwater sonar frames.
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Wang, Jinkang, Xiaohui He, Faming Shao, Guanlin Lu, Qunyan Jiang, Ruizhe Hu, and Jinxin Li. "A Novel Attention-Based Lightweight Network for Multiscale Object Detection in Underwater Images." Journal of Sensors 2022 (September 7, 2022): 1–14. http://dx.doi.org/10.1155/2022/2582687.

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Underwater images have low quality, and underwater targets have different sizes. The mainstream target detection networks cannot achieve good results in detecting objects from underwater images. In this study, a lightweight underwater multiscale target detection model with an attention mechanism is designed to solve the above problems. In this model, MobileNetv3 is used as the backbone network for preliminary feature extraction. The lightweight feature extraction module (LFEM) pays attention to the feature map at the channel and space levels. The features with large weights are promoted, while the features with small weights are suppressed. Meanwhile, cross-group information exchange enriches the semantic information and location information of the objects. The context aggregation module (CIAM) pools the extracted feature maps to obtain feature pyramids, and it uses the upsampling-feature refinement-cascade addition (URC) method to effectively fuse global context information and enhance the feature representation. The scale normalization for feature pyramids (SNFP) performs adaptive multiscale perception and multianchor detection on feature maps to cover objects of different sizes and realize multiscale object detection in underwater images. The proposed network can realize lightweight feature extraction, effectively handle the global relationship between the underwater scene and the object while expanding the receptive field, traverse the objects of different scales, and achieve adaptive multianchor detection of multiscale objects in underwater images. The experimental results indicate that our method achieves an average accuracy of 81.94% and a detection speed of 44.3 FPS on a composite dataset. Also, our method is better than the mainstream object detection networks in terms of detection accuracy, lightweight design, and real-time performance.
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Wulandari, Nurcahyani, Igi Ardiyanto, and Hanung Adi Nugroho. "A Comparison of Deep Learning Approach for Underwater Object Detection." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 6, no. 2 (April 20, 2022): 252–58. http://dx.doi.org/10.29207/resti.v6i2.3931.

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In recent year, marine ecosystems and fisheries becomes potential resources, therefore, monitoring of these objects will be important to ensure their existence. One of computer vision techniques, it is object detection, utilized to recognize and localize objects in underwater scenery. Many studies have been conducted to investigate various deep learning methods implemented in underwater object detection; however, only a few investigations have been performed to compare mainstream object detection algorithms in these circumstances. This article examines various state-of-the-art deep learning methods applied to underwater object detection, including Faster-RCNN, SSD, RetinaNet, YOLOv3, and YOLOv4. We trained five models on RUIE dataset, then the average detection time used to compare how fast a model can detect object within an image; and mAP also applied to measured detection accuracy. All trained models have costs and benefits; SSD was fast but had poor performance; RetinaNet had consistent performance across different thresholds but the detection speed was slow; YOLOv3 was the fastest and had sufficient performance comparable with RetinaNet; YOLOv4 was good at first but performance dropped as threshold enlargement; also, YOLOv4 needed extra time to detect objects compared to YOLOv3. There are no models that are fully suited for underwater object detection; nonetheless, when the mAP and average detection time of the five models were compared, we determined that YOLOv3 is the best acceptable model among the evaluated underwater object detection models.
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Dissertations / Theses on the topic "Underwater object detection"

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Wang, Qiang 1968. "Underwater object localization using a biomimetic binaural sonar." Thesis, Massachusetts Institute of Technology, 1999. http://hdl.handle.net/1721.1/80359.

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Thesis (S.M. in Oceanographic Engineering)--Joint Program in Applied Ocean Science and Engineering (Massachusetts Institute of Technology, Dept. of Ocean Engineering; and the Woods Hole Oceanographic Institution), 1999.
Includes bibliographical references (leaves 85-89).
by Qiang Wang.
S.M.in Oceanographic Engineering
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Du, Pisani Renaldo Murray. "Design of an Underwater Object Detection and Location System using Wide-Beam SONAR." Thesis, Stellenbosch : Stellenbosch University, 2014. http://hdl.handle.net/10019.1/86236.

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Thesis (MScEng)--Stellenbosch University, 2014.
ENGLISH ABSTRACT: This thesis describes the second project relating to the development of a SONAR (SOund Navigation And Ranging) object detection and collision avoidance system for use on an AUV (Autonomous Underwater Vehicle) at Stellenbosch University. The main goal is to develop and test techniques that make use of the existing SONAR laboratory platform and wide-beam SONAR transducers to detect and locate objects and their limits/bounds under water in the horizontal plane. The results of the work done show that it is possible to use wide-beam transducers to locate the centroid and edges of a flat target with an error that is significantly smaller than the beam-width. The techniques developed will enable the development of a cost-effective SONAR system that can be implemented on an AUV.
AFRIKAANSE OPSOMMING: Hierdie tesis beskryf the tweede projek rakende die ontwikkeling van ’n SONAR voorwerp opsporings en botsingvermydingstelsel vir gebruik op ’n OOV (Outonome Onderwater Voertuig) aan die Universiteit van Stellenbosch. Die hoofdoel is om tegnieke te ontwikkel en te toets wat gebruik maak van die bestaande SONAR laboratorium opstelling en wye-straal SONAR opnemers om die posisie van voorwerpe onder water te bepaal, sowel as die posisie van die voorwerp se rande in die horisontale vlak. Die resultate van die werk wat gedoen is wys dat dit moontlik is om wye-straal opnemers te gebruik om die posisie van die sentroïde en rande van ’n plat voorwerp te vind met ’n fout wat aansienlik kleiner is as die straal-wydte. Die tegnieke wat ontwikkel is sal ons in staat stel om ’n koste-effektiewe SONAR stelsel te ontwikkel wat op ’n OOV geïmplenteer kan word.
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Himri, Khadidja. "Automated 3D object recognition in underwater scenarios for manipulation." Doctoral thesis, Universitat de Girona, 2021. http://hdl.handle.net/10803/673811.

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In recent decades, the rapid development of intelligent vehicle and 3D scanning tecnologies has led to a growing interest in applications based on 3D point data processing, with many applications such as augmented reality or robot manipulation and obstacle avoidance, scene understanding, robot navigation, tracking and assistive technology among others, requiring an accurate solution for the 3D pose of the recognized objects. Thus object recognition is becoming an important topic in computer vision, where machine vision and robotics techniques are becoming key players. In this thesis work, the main objective is to develop a semantic mapping method by integrating a 3D object recognition pipeline with a feature-based SLAM system, in order to assist autonomous underwater interventions in the near future. To this end, the work proposed in this paper targets three axes. First, it aims to compare the performance of 3D global descriptors within the state of the art, focusing on those based on point clouds and targeted at real-time object recognition applications. For this purpose, we selected a set of test objects representative of Inspection, Maintenance and Repair (IMR) applications and whose shape is usually known a priori. Their CAD models were used to: 1) create a data base of synthetic object views used as a priori knowledge, and 2) simulate the point clouds that would be gathered during the scanning under realistic conditions, with added noise and varying resolution. Extensive experiments were performed with both virtual scans and real data collected with an AUV equipped with a fast laser scanner developed at our research centre. The second goal of our work was to use a real-time laser scanner mounted on an AUV to detect, identify, and locate objects in the robot’s environment, with the aim of allowing an intervention Autonomous Underwater Vehicle (I-AUV) to know what manipulation actions could be performed on each object. This goal was tackled by the design and development of a 3D object recognition method for uncolored point clouds (laser scans) using point features. The algorithm uses a database of partial views of the objects stored as point clouds. The recognition pipeline includes 5 stages: 1) Plane segmentation, 2) Pipe detection, 3) Semantic Object-segmentation, 4) Feature-based Object Recognition and 5) Bayesian estimation. To apply Bayesian estimation, it is necessary to track objects across scans. For this purpose, the Inter-distance Joint Compatibility Branch and Bound (IJCBB) data association algorithm was proposed based on the distances between objects. The performance of the method was tested using a dataset of the inspection of a pipe infrastructure made of PVC objects connected by pipes. The structure is representative of those commonly used by the offshore industry. Experimental results show that Bayesian estimation improves the recognition performance with respect to the case where only the descriptor is used. The inclusion of semantic information about object pipe connectivity further improves recognition performance. The final goal of the thesis, consists of integrating the 3D object recognition system with a feature-based SLAM system to implement a semantic map providing the robot with information about the location and the type of objects in its surroundings. The SLAM improved both the accuracy and reliability of pose estimates of the robot and the objects. This is especially important in challenging scenarios where significant changes in viewpoint and appearance arise
A les darreres dècades, el ràpid desenvolupament de vehicles intel·ligents i de les tecnologies d’escaneig 3D han contribuït a augmentar l’interès en les aplicacions basades en processament de núvols de punts 3D, amb aplicacions com la realitat augmentada, la manipulació robòtica, l’evasió d’obstacles, la comprensió d’escenes, la navegació robòtica, el seguiment d’objectes i la tecnologia d’assistència, etc., que requereixen una soluci´o precisa de la posició 3D i l’orientació d’un objecte. Per tant, el reconeixement d’objectes s’està convertint en un tema, on la visió per computador i les tècniques robòtiques esdevenen protagonistes clau. En aquest treball de tesi, l’objectiu principal és desenvolupar un mètode per a la construcció de mapes semàntic mitjançant la integració d’una cadena de processament per al reconeixement d’objectes 3D, amb un sistema de SLAM basat en característiques, amb l’objectiu d’ajudar a les futures intervencions submarines. Per això, el treball proposat en aquesta tesi es divideix en tres eixos principals. El primer té com a objectiu comparar el rendiment de descriptors globals d’última generació, centrant-se en els basats en núvols de punts 3D i destinats a aplicacions de reconeixement d’objectes en temps real. Per a aquest objectiu, s’ha seleccionat un conjunt d’objectes de prova representatius d’aplicacions d’inspecció, manteniment i reparació (IMR), la forma dels quals es coneix a priori. Els seus models CAD s’han utilitzat per a: 1) crear una base de dades amb les vistes sintètiques dels objectes, i 2) simular els núvols de punts que adquiriria, en condicions realistes, un escàner làser incloent soroll sintètic i simulant diferents resolucions. S’han dut a terme experiments tant a partir d’escaneigs virtuals com de dades reals recopilades amb un AUV equipat amb un escàner làser de temps real desenvolupat al nostre centre de recerca. El segon objectiu del nostre treball va consistir en utilitzar aquest escàner làser, muntat a un AUV per detectar, reconèixer i localitzar objectes a l’entorn del robot, per tal de permetre, a un Vehicle Submarí Autònoms d’Intervenció (IAUV), saber quines accions de manipulació podria fer amb cada objecte. Aquest objectiu es va abordar amb el disseny i el desenvolupament d’un mètode de reconeixement d’objectes 3D en núvols de punts incolors (escanejos làser) utilitzant descriptors dels punts 3D. L’algorisme utilitza una base de dades de vistes parcials dels objectes emmagatzemats en forma de núvols de punts. El procés de reconeixement consta de 5 passos: 1) Segmentació de plànols, 2) Detecció de canonades, 3) Segmentació semàntica d’objectes, 4) Reconeixement d’objectes a partir dels descriptors de punts 3D i 5) Estimació bayesiana. Per aplicar l’estimació bayesiana, cal ser capaços de fer un seguiment dels objectes en escanejos successius. Per fer-ho, s’ha proposat l’algorisme Inter-distance Joint-Compatibility Branch and Bound (IJCBB) d’associació de dades basada en les distancies entre objectes dins del núvol de punts. El rendiment del mètode es va avaluar fent servir dades experimentals relatives a la inspecció d’una infraestructura composta de canonades interconnectades per objectes de PVC. L’estructura ´es representativa de les comunament utilitzades per la indústria offshore. Els resultats experimentals mostren que l’estimació bayesiana millora el rendiment del reconeixement en comparació de l’ús ´únic del descriptor. La inclusió d’informació semàntica sobre la connectivitat d’objectes a canonades millora encara més el rendiment del reconeixement. L’objectiu final de la tesi va abordar la integració del sistema de reconeixement d’objectes 3D basat en descriptors amb un sistema de SLAM basat en característiques, per implementar un mapa semàntic que proporciona al robot informació sobre la ubicació i el tipus d’objectes a l’entorn. La utilització de tècniques de SLAM ha millorat la precisió i la fiabilitat de les estimacions de la postura del robot i els objectes. Això és especialment important en escenaris difícils on es produeixen canvis significatius de perspectiva i aparença
Programa de Doctorat en Tecnologia
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Moniruzzaman, Md. "Seagrass detection using deep learning." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2019. https://ro.ecu.edu.au/theses/2261.

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Seagrasses play an essential role in the marine ecosystem by providing foods, nutrients, and habitat to the marine lives. They work as marine bioindicators by reflecting the health condition of aquatic environments. Seagrasses also act as a significant atmospheric carbon sink that mitigates global warming and rapid climate changes. Considering the importance, it is critical to monitor seagrasses across the coastlines which includes detection, mapping, percentage cover calculation, and health estimation. Remote sensing-based aerial and spectral images, acoustic images, underwater two-dimensional and three-dimensional digital images have so far been used to monitor seagrasses. For close monitoring, different machine learning classifiers such as the support vector machine (SVM), the maximum likelihood classifier (MLC), the logistic model tree (LMT) and the multilayer perceptron (MP) have been used for seagrass classification from two-dimensional digital images. All of these approaches used handcrafted feature extraction methods, which are semi-automatic. In recent years, deep learning-based automatic object detection and image classification have achieved tremendous success, especially in the computer vision area. However, to the best of our knowledge, no attempts have been made for using deep learning for seagrass detection from underwater digital images. Possible reasons include unavailability of enough image data to train a deep neural network. In this work, we have proposed a Faster R-CNN architecture based deep learning detector that automatically detects Halophila ovalis (a common seagrass species) from underwater digital images. To train the object detector, we have collected a total of 2,699 underwater images both from real-life shorelines, and from an experimental facility. The selected seagrass (Halophila ovalis) are labelled using LabelImg software, commonly used by the research community. An expert in seagrass reviewed the extracted labels. We have used VGG16, Resnet50, Inception V2, and NASNet in the Faster R-CNN object detection framework which were originally trained on COCO dataset. We have applied the transfer learning technique to re-train them using our collected dataset to be able to detect the seagrasses. Inception V2 based Faster R-CNN achieved the highest mean average precision (mAP) of 0.261. The detection models proposed in this dissertation can be transfer learned with labelled two-dimensional digital images of other seagrass species and can be used to detect them from underwater seabed images automatically.
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Olmos, Antillon Adriana Teresa. "Detecting underwater man-made objects in unconstrained video images." Thesis, Heriot-Watt University, 2002. http://hdl.handle.net/10399/1172.

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Dumortier, Alexis Jean Louis. "Detection, classification and localization of seabed objects with a virtual time reversal mirror." Thesis, Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1721.1/55316.

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Thesis (S.M.)--Joint Program in Oceanography/Applied Ocean Science and Engineering (Massachusetts Institute of Technology, Dept. of Mechanical Engineering; and the Woods Hole Oceanographic Institution), 2009.
Includes bibliographical references (p. 88-91).
The work presented in this thesis addresses the problem of the detection, classification and localization of seabed objects in shallow water environments using a time reversal approach in a bistatic configuration. The waveguide is insonified at low frequency ('kHz) with an omnidirectional source and the resulting scattered field is sampled by a receiving array towed behind an Autonomous Underwater Vehicle (AUV). The recorded signals are then processed to simulate onboard the AUV, the time reversed transmissions which serve to localize the origin of the scattered field on the seabed and estimate the position of the targets present. The clutter rejection based upon the analysis of the singular values of the Time Reversal operator is investigated with simulated data and field measurements collected off the coast of Palmaria (Italy) in January 2008.
by Alexis J. Dumortier.
S.M.
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Léonard, Isabelle. "Reconnaissance des objets manufacturés dans des vidéos sous-marines." Phd thesis, Université de Bretagne occidentale - Brest, 2012. http://tel.archives-ouvertes.fr/tel-00780647.

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Les mines sous marines sont très utilisées dans les conflits. Pour contrer cette menace, les marines s'équipent de moyens de lutte anti-mine autonomes afin d'éviter l'intervention humaine. Une mission de guerre des mines se découpe en quatre étapes distinctes : la détection des objets, la classification et l'identification puis la neutralisation. Cette thèse propose des solutions algorithmiques pour l'étape d'identification par caméra vidéo. Le drone d'identification connaît la position approximative de l'objet à identifier. La première mission de ce drone est de re-détecter l'objet avant de le classifier et de l'identifier. Le milieu sous-marin perturbe les images acquises par la caméra (absorption, diffusion). Pour faciliter la détection et la reconnaissance (classification et identification), nous avons prétraité les images. Nous avons proposé deux méthodes de détection des objets. Tout d'abord nous modifions le spectre de l'image afin d'obtenir une image dans laquelle il est possible de détecter les contours des objets. Une seconde méthode a été développée à partir de la soustraction du fond, appris en début de séquence vidéo. Les résultats obtenus avec cette seconde méthode ont été comparés à une méthode existante. Lorsqu'il y a une détection, nous cherchons à reconnaître l'objet. Pour cela, nous utilisons la corrélation. Les images de référence ont été obtenues à partir d'images de synthèse 3D des mines. Pour les différentes méthodes utilisées, nous avons optimisés les résultats en utilisant les informations de navigation. En effet, selon les déplacements du drone, nous pouvons fixer des contraintes qui vont améliorer la détection et réduire le temps de calcul nécessaire à l'identification.
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Chou, Ching-Chin, and 周靜歆. "Underwater object detection and positoning with usage of the Ultrashort Baseline method." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/82305438767289048656.

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Liu, Chi-Chung, and 廖志忠. "Incorporating Object Detection and Stereo Vision for Real-time Underwater Fish Range Measurement." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/m597xm.

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Wang, Bo-Sen, and 王柏森. "Development of a high resolution laser Doppler vibrometer for the detection of underwater object motion." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/84731069142129288594.

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Book chapters on the topic "Underwater object detection"

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Priyadarshni, Divya, and MaheshKumar H. Kolekar. "Underwater Object Detection and Tracking." In Advances in Intelligent Systems and Computing, 837–46. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-0751-9_76.

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Fan, Baojie, Wei Chen, Yang Cong, and Jiandong Tian. "Dual Refinement Underwater Object Detection Network." In Computer Vision – ECCV 2020, 275–91. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58565-5_17.

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Sarkar, Pratima, Sourav De, and Sandeep Gurung. "A Survey on Underwater Object Detection." In Intelligence Enabled Research, 91–104. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0489-9_8.

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Pagire, Vrushali, and Anuradha Phadke. "Underwater Moving Object Detection Using GMG." In Advances in Intelligent Systems and Computing, 233–44. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-73689-7_23.

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Huang, Andi, Guoqiang Zhong, Hao Li, and Daewon Choi. "Underwater Object Detection Using Restructured SSD." In Artificial Intelligence, 526–37. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-20497-5_43.

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Yu, Junzhi, Xingyu Chen, and Shihan Kong. "Rethinking Temporal Object Detection from Robotic Perspectives." In Visual Perception and Control of Underwater Robots, 125–46. First edition. | Boca Raton, FL : CRC Press/Taylor & Francis Group, LLC, 2021.: CRC Press, 2021. http://dx.doi.org/10.4324/9781003144281-5.

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Yu, Junzhi, Xingyu Chen, and Shihan Kong. "Rethinking Temporal Object Detection from Robotic Perspectives." In Visual Perception and Control of Underwater Robots, 125–46. First edition. | Boca Raton, FL : CRC Press/Taylor & Francis Group, LLC, 2021.: CRC Press, 2021. http://dx.doi.org/10.1201/9781003144281-5.

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Wu, Meng, and Jian Yao. "Magnetic–Gravity Gradient Inversion for Underwater Object Detection." In Intelligent Robotics and Applications, 549–55. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-22879-2_50.

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Moniruzzaman, Md, Syed Mohammed Shamsul Islam, Mohammed Bennamoun, and Paul Lavery. "Deep Learning on Underwater Marine Object Detection: A Survey." In Advanced Concepts for Intelligent Vision Systems, 150–60. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70353-4_13.

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Lv, Xiaoqian, An Wang, Qinglin Liu, Jiamin Sun, and Shengping Zhang. "Proposal-Refined Weakly Supervised Object Detection in Underwater Images." In Lecture Notes in Computer Science, 418–28. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-34120-6_34.

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Conference papers on the topic "Underwater object detection"

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Dakhil, Radhwan Adnan, and Ali Retha Hasoon Khayeat. "Review on Deep Learning Techniques for Underwater Object Detection." In 3rd International Conference on Data Science and Machine Learning (DSML 2022). Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.121505.

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Abstract:
Repair and maintenance of underwater structures as well as marine science rely heavily on the results of underwater object detection, which is a crucial part of the image processing workflow. Although many computer vision-based approaches have been presented, no one has yet developed a system that reliably and accurately detects and categorizes objects and animals found in the deep sea. This is largely due to obstacles that scatter and absorb light in an underwater setting. With the introduction of deep learning, scientists have been able to address a wide range of issues, including safeguarding the marine ecosystem, saving lives in an emergency, preventing underwater disasters, and detecting, spooring, and identifying underwater targets. However, the benefits and drawbacks of these deep learning systems remain unknown. Therefore, the purpose of this article is to provide an overview of the dataset that has been utilized in underwater object detection and to present a discussion of the advantages and disadvantages of the algorithms employed for this purpose.
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Williams, D. P. "On adaptive underwater object detection." In 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2011). IEEE, 2011. http://dx.doi.org/10.1109/iros.2011.6094621.

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Williams, David P. "On adaptive underwater object detection." In 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2011). IEEE, 2011. http://dx.doi.org/10.1109/iros.2011.6048234.

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Gomes, Dipta, A. F. M. Saifuddin Saif, and Dip Nandi. "Robust Underwater Object Detection with Autonomous Underwater Vehicle." In ICCA 2020: International Conference on Computing Advancements. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3377049.3377052.

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Rizos, Panagiotis, and Vana Kalogeraki. "Deep Learning for Underwater Object Detection." In PCI 2020: 24th Pan-Hellenic Conference on Informatics. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3437120.3437301.

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Peng, Yan-Tsung, Yu-Cheng Lin, and Wen-Yi Peng. "Blurriness Guided Underwater Salient Object Detection." In OCEANS 2021: San Diego – Porto. IEEE, 2021. http://dx.doi.org/10.23919/oceans44145.2021.9705721.

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Sun, Yuqi, Xuan Wang, Yi Zheng, Lin Yao, Shuhan Qi, Linlin Tang, Hong Yi, and Kunlei Dong. "Underwater Object Detection with Swin Transformer." In 2022 4th International Conference on Data Intelligence and Security (ICDIS). IEEE, 2022. http://dx.doi.org/10.1109/icdis55630.2022.00070.

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Wang, Xiaohan, Xiaoyue Jiang, Zhaoqiang Xia, and Xiaoyi Feng. "Underwater Object Detection Based on Enhanced YOLO." In 2022 International Conference on Image Processing and Media Computing (ICIPMC). IEEE, 2022. http://dx.doi.org/10.1109/icipmc55686.2022.00012.

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Liu, Hong, Pinhao Song, and Runwei Ding. "Towards Domain Generalization In Underwater Object Detection." In 2020 IEEE International Conference on Image Processing (ICIP). IEEE, 2020. http://dx.doi.org/10.1109/icip40778.2020.9191364.

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Wang, Yudong, Jichang Guo, and Wanru He. "Underwater Object Detection Aided by Image Reconstruction." In 2022 IEEE 24th International Workshop on Multimedia Signal Processing (MMSP). IEEE, 2022. http://dx.doi.org/10.1109/mmsp55362.2022.9949063.

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Reports on the topic "Underwater object detection"

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Epstein, Zachary, and Phillip Sprangle. Optical Magnetometry for Detecting Underwater Objects. Fort Belvoir, VA: Defense Technical Information Center, September 2015. http://dx.doi.org/10.21236/ad1000476.

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