Academic literature on the topic 'Underwater object detection'
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Journal articles on the topic "Underwater object detection"
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.
Full textV, 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.
Full textMaccabee, 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.
Full textMahavarkar, 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.
Full textZhang, 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.
Full textZHANG, 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.
Full textShen, 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.
Full textKarimanzira, 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.
Full textWang, 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.
Full textWulandari, 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.
Full textDissertations / Theses on the topic "Underwater object detection"
Wang, Qiang 1968. "Underwater object localization using a biomimetic binaural sonar." Thesis, Massachusetts Institute of Technology, 1999. http://hdl.handle.net/1721.1/80359.
Full textIncludes bibliographical references (leaves 85-89).
by Qiang Wang.
S.M.in Oceanographic Engineering
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.
Full textENGLISH 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.
Himri, Khadidja. "Automated 3D object recognition in underwater scenarios for manipulation." Doctoral thesis, Universitat de Girona, 2021. http://hdl.handle.net/10803/673811.
Full textA 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
Moniruzzaman, Md. "Seagrass detection using deep learning." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2019. https://ro.ecu.edu.au/theses/2261.
Full textOlmos, Antillon Adriana Teresa. "Detecting underwater man-made objects in unconstrained video images." Thesis, Heriot-Watt University, 2002. http://hdl.handle.net/10399/1172.
Full textDumortier, 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.
Full textIncludes 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.
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.
Full textChou, 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.
Full textLiu, 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.
Full textWang, 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.
Full textBook chapters on the topic "Underwater object detection"
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.
Full textFan, 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.
Full textSarkar, 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.
Full textPagire, 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.
Full textHuang, 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.
Full textYu, 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.
Full textYu, 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.
Full textWu, 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.
Full textMoniruzzaman, 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.
Full textLv, 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.
Full textConference papers on the topic "Underwater object detection"
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.
Full textWilliams, 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.
Full textWilliams, 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.
Full textGomes, 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.
Full textRizos, 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.
Full textPeng, 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.
Full textSun, 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.
Full textWang, 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.
Full textLiu, 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.
Full textWang, 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.
Full textReports on the topic "Underwater object detection"
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.
Full text