Automatic Tool for Coral Reef Detection, Localization and Annotation

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F20%3A43960896" target="_blank" >RIV/49777513:23520/20:43960896 - isvavai.cz</a>

Result on the web

<a href="http://www.kky.zcu.cz/cs/sw/corals" target="_blank" >http://www.kky.zcu.cz/cs/sw/corals</a>

DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Automatic Tool for Coral Reef Detection, Localization and Annotation

Original language description

The created tool is designed to automatically detect and annotate various benthic substrate (Corals) types over image collections taken from multiple coral reefs as part of a coral reef monitoring project with the Marine Technology Research Unit at the University of Essex. Live corals are an important biological class that has a massive contribution to the ocean ecosystem biodiversity. Corals are a key habitat for thousands of marine species and provide an essential source of nutrition and yield for people in developing countries. Therefore, automatic monitoring of coral reefs condition plays a crucial part in understanding future threats and prioritizing conservation efforts. The system&apos;s performance was validated in the international ImageCLEFcoral competition, where achieve an impressive mAP@0.5 of 0.582 in localization and 0.678 in instance segmentation. The system is wrapped up around the Mask R-CNN, the state-of-the-art instance segmentation framework and the TensorFlow Object Detection API - a deep learning framework that allows fine-tuning the publicly available checkpoints. To increase the model performance, we extend the recent state-of-the-art Convolutional Neural Network (CNN) object detection framework (Mask R-CNN) with an additional known as well as some unique techniques, e.g., detection ensemble, test time data augmentations, accumulated gradient normalization, and pseudo-labelling. The &quot;tool&quot; works as an end-to-end system that takes an image as input and returns 2D locations and substrate type for each prediction. Both the pre-trained models and the Application Interface (API) were made OpenSource to support further research in this area. The software was created by researchers from the Faculty of Applied Sciences - University of West Bohemia (UWB) and the Institute of Information Theory and Automation - Czech Academy of Sciences. Lukas Picek from the UWB was supported by the UWB grant SGS-2019-027.

Czech name

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Czech description

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Type

R - Software

CEP classification

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OECD FORD branch

20205 - Automation and control systems

Project

<a href="/en/project/LO1506" target="_blank" >LO1506: Sustainability support of the centre NTIS - New Technologies for the Information Society</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2020

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Internal product ID

ZCU/KKY/2020/015

Technical parameters

Tento softwarový nástroj používá pokročilé metody strojového učení. Pro bližší informace k technickým detailům a získání licence prosím kontaktujte: Lukáš Picek, KKY FAV Západočeská univerzita v Plzni, Technická 8, 301 00 Plzeň (IČ: 49777513), email: picekl@kky.zcu.cz, tel. 37763 2125.

Economical parameters

Tento SW nástroj je volně dostupný pro využití nekomerčního charakteru. Vzhledem k významnosti výstupu v oblasti vzdělávání a souvisejícímu společenskému dopadu nelze zhodnotit ekonomické parametry SW výstupu - operativní výzkum ve veřejném zájmu.

Owner IČO

49777513

Owner name

Západočeská univerzita v Plzni