Detection and removal of jellyfish using underwater image analysis

Matsuura, Fuminori; Fujisawa, Nobuyuki; Ishikawa, Shota

doi:10.1007/bf03181692

Cited by 10 publications

(2 citation statements)

References 4 publications

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“…A number of studies attempted underwater imagery collection and analysis [17]- [21], however, did not gather enough data to adequately evaluate the performance, hence it is unknown how they would process long-term observations. Some systems did not consider real life conditions and were tested on synthetic data [22] or in manually set up environments where the presence of noise is minimal e.g., well lit tanks [23]. The idea of slicing jellyfish by cutting nets or blades showed its effectiveness [9], [24]; we deviate from that approach as upon being attacked, medusae release large amounts of eggs and sperm that later form planulas, thus amplifying population increase in the future.…”

Section: Related Workmentioning

confidence: 99%

Improving Automated Sonar Video Analysis to Notify About Jellyfish Blooms

et al. 2021

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Human enterprise often suffers from direct negative effects caused by jellyfish blooms. The investigation of a prior jellyfish monitoring system showed that it was unable to reliably perform in a cross validation setting, i.e. in new underwater environments. In this paper, a number of enhancements are proposed to the part of the system that is responsible for object classification. First, the training set is augmented by adding synthetic data, making the deep learning classifier able to generalise better. Then, the framework is enhanced by employing a new second stage model, which analyzes the outputs of the first network to make the final prediction. Finally, weighted loss and confidence threshold are added to balance out true and false positives. With all the upgrades in place, the system can correctly classify 30.16% (comparing to the initial 11.52%) of all spotted jellyfish, keep the amount of false positives as low as 0.91% (comparing to the initial 2.26%) and operate in real-time within the computational constraints of an autonomous embedded platform.

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Section: Related Workmentioning

confidence: 99%

Improving Automated Sonar Video Analysis to Notify About Jellyfish Blooms

et al. 2021

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“…발전소에서는 취수구 해파 리 유입 방지를 위해 기포를 이용한 해파리 부양 및 인양 장치와 카메라와 펌프를 이용한 해파리 인식 및 배출 장치 [5] 가 개발되었다. 하지만 이 연구들은 많은 인력을 필요로 하거나, 자율 운용이 어려운 단점이 있다.…”

Section: 서 론unclassified

Design and Implementation of Unmanned Surface Vehicle JEROS for Jellyfish Removal

Kim¹,

Shin²,

Kim³

et al. 2013

The Journal of Korea Robotics Society

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Recently, the number of jellyfish has been rapidly grown because of the global warming, the increase of marine structures, pollution, and etc. The increased jellyfish is a threat to the marine ecosystem and induces a huge damage to fishery industries, seaside power plants, and beach industries. To overcome this problem, a manual jellyfish dissecting device and pump system for jellyfish removal have been developed by researchers. However, the systems need too many human operators and their benefit to cost is not so good. Thus, in this paper, the design, implementation, and experiments of autonomous jellyfish removal robot system, named JEROS, have been presented. The JEROS consists of an unmanned surface vehicle (USV), a device for jellyfish removal, an electrical control system, an autonomous navigation system, and a vision-based jellyfish detection system. The USV was designed as a twin hull-type ship, and a jellyfish removal device consists of a net for gathering jellyfish and a blades-equipped propeller for dissecting jellyfish. The autonomous navigation system starts by generating an efficient path for jellyfish removal when the location of jellyfish is received from a remote server or recognized by a vision system. The location of JEROS is estimated by IMU (Inertial Measurement Unit) and GPS, and jellyfish is eliminated while tracking the path. The performance of the vision-based jellyfish recognition, navigation, and jellyfish removal was demonstrated through field tests in the Masan and Jindong harbors in the southern coast of Korea.

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