ArticleSensors (Basel, Switzerland)2020
Jellytoring: Real-Time Jellyfish Monitoring Based on Deep Learning Object Detection.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 62 citations in OpenAlex.
- Enhanced deep learning models for automatic fish species identification in underwater imagery.Heliyon · 2024Article
- Real-time precision detection algorithm for jellyfish stings in neural computing, featuring adaptive deep learning enhanced by an advanced YOLOv4 framework.Frontiers in neurorobotics · 2024Article
- Real-time jellyfish classification and detection algorithm based on improved YOLOv4-tiny and improved underwater image enhancement algorithm.Scientific reports · 2023Article
- Jellyfishes-Significant Marine Resources with Potential in the Wound-Healing Process: A Review.Marine drugs · 2023Review
- A physics-based model of swarming jellyfish.PloS one · 2023Article
- An overview of remote monitoring methods in biodiversity conservation.Environmental science and pollution research international · 2022Review
- The potential of Jellytoring 2.0 smart tool as a global jellyfish monitoring platform.Ecology and evolution · 2022Article
- Real-Time Pipe and Valve Characterisation and Mapping for Autonomous Underwater Intervention Tasks.Sensors (Basel, Switzerland) · 2022Article
- Article
- Real-Time Jellyfish Classification and Detection Based on Improved YOLOv3 Algorithm.Sensors (Basel, Switzerland) · 2021Article
- Life beneath the ice: jellyfish and ctenophores from the Ross Sea, Antarctica, with an image-based training set for machine learning.Biodiversity data journal · 2021Article
- Jellytoring: Real-Time Jellyfish Monitoring Based on Deep Learning Object Detection.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
During the past decades, the composition and distribution of marine species have changed due to multiple anthropogenic pressures. Monitoring these changes in a cost-effective manner is of high relevance to assess the environmental status and evaluate the effectiveness of management measures. In particular, recent studies point to a rise of jellyfish populations on a global scale, negatively affecting diverse marine sectors like commercial fishing or the tourism industry. Past monitoring efforts using underwater video observations tended to be time-consuming and costly due to human-based data processing. In this paper, we present Jellytoring, a system to automatically detect and quantify different species of jellyfish based on a deep object detection neural network, allowing us to automatically record jellyfish presence during long periods of time. Jellytoring demonstrates outstanding performance on the jellyfish detection task, reaching an
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.