In situ real-time Zooplankton Detection and Classification

• Type: Article in International Conference Proceedings Book
• Date: 2019

Authors

Geraldes, P (Author) Other; Barbosa, J (Author) Other; Martins, A (Author) Other; Dias, A (Author) Other; Magalhaes, C (Author) FCUP; Ramos, S (Author) Other; E. Pereira da Silva (Author) Other

Conference proceedings International

Title: OCEANS 2019 - Marseille

Indexing

ISI Web of Knowledge - 1 Citation

Scopus - 4 Citations

Other information

Authenticus ID: P-00R-P5J

DOI: 10.1109/oceanse.2019.8867552

Abstract (EN): Zooplankton plays a key -role on Earth's ecosystem, emerging in the oceans and rivers in great quantities and diversity, making it an important and rather common topic on scientific studies. Given the numbers of different species it is not only necessary to study their numbers but also their classification. In this paper a possible solution for the zooplankton in situ detection and classification problem in real-time is proposed using a portable deep learning approach based on Convolutional Neural Networks deployed on INESC TEC's MarinEye system. For detection a Single Shot Detection model with MobileNet was used, and ZooplanktoNet for classification. System portability is guaranteed with the use of MovidiusTMNeural Compute Stick as the deep learning motor.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 6