Improving Automated Sonar Video Analysis to Notify About Jellyfish Blooms
| Autor: | Mike Challiss, Peter Knight, Michal Mackiewicz, Artjoms Gorpincenko, Geoffrey French |
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| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Jellyfish biology Computer science business.industry Computer Vision and Pattern Recognition (cs.CV) Deep learning 010401 analytical chemistry Computer Science - Computer Vision and Pattern Recognition Object (computer science) Machine learning computer.software_genre 01 natural sciences Sonar 0104 chemical sciences biology.animal False positive paradox 14. Life underwater Artificial intelligence Electrical and Electronic Engineering business Instrumentation computer |
| Zdroj: | IEEE Sensors Journal. 21:4981-4988 |
| ISSN: | 2379-9153 1530-437X |
| DOI: | 10.1109/jsen.2020.3032031 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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