FN Archimer Export Format
PT C
TI Real-Time Automatic Plankton Detection, Tracking and Classification on Raw Hologram
BT 
AF Scherrer, Romane
   Govan, Rodrigue
   Quiniou, Thomas
   Jauffrais, Thierry
   Lemonnier, Hugues
   Bonnet, Sophie
   Selmaoui-Folcher, Nazha
AS 1:1;2:1;3:1;4:2;5:2;6:3;7:1;
FF 1:;2:;3:;4:PDG-RBE-LEADNC;5:PDG-RBE-LEADNC;6:;7:;
C1 ISEA, Universite de la Nouvelle-Caledonie, Noumea, New-Caledonia
   Ifremer, UMR9220 Entropie, Noumea, New-Caledonia
   Aix Marseille Univ, Universit´e de Toulon, CNRS, IRD, MIO, Marseille, France
C2 UNIV NOUVELLE CALEDONIE, FRANCE
   IFREMER, FRANCE
   UNIV AIX MARSEILLE, FRANCE
SI NOUMEA
SE PDG-RBE-LEADNC
UM ENTROPIE
IN WOS Ifremer UMR
   copubli-france
   copubli-univ-france
UR https://archimer.ifremer.fr/doc/00807/91900/97861.pdf
LA English
DT Proceedings paper
DE ;Object detection;Multiple Object Tracking;Deep learning;Plankton;Digital holography
AB Digital holography is an imaging process that encodes the 3D information of objects into a single intensity image. In recent years, this technology has been used to detect and count various microscopic objects and has been applied in submersible equipment to monitor the in situ distribution of plankton. To count and classify plankton, conventional methods require a holographic reconstruction step to decode the hologram before identifying the objects. However, this iterative and time-consuming step must be performed at each frame of a video, which makes it difficult to support real-time processing. We propose a real-time object detection based approach that simultaneously performs the detection, classification and counting of all plankton within videos of raw holograms. Experiments show that our pipeline based on YOLOv5 and SORT is fast (44 FPS) and can accurately detect and identify the plankton among 13 classes (97.6% mAP@0.5, 92% MOTA). Our method can be implemented to detect and count other microscopic objects in raw holograms. 
PY 2022
CT CIBB 2021: Computational Intelligence Methods for Bioinformatics and Biostatistics proceedings. Part of the Lecture Notes in Computer Science book series (LNBI,volume 13483). Springer, Cham. pp 25–39
DI 10.1007/978-3-031-20837-9_3
ID 91900
ER

EF