In this project we examined the automatic seagrass coverage estimation of the sea bottom. A total of 12682 images of the seabed at different depths along Croatia's Adriatic coast were taken with the help of a diving robot. Of these, 6036 images were manually polygon-annotated by hand and made available to the public as pixel maps. Using this dataset, we tested a superpixel classification of seagrass images. To achieve this, we used several different feature extraction methods as for example CNN-Features that turned out to be the best ones in our experiments.
This project is a joint work between University of Zadar - Croatia and University of Applied Sciences Fulda - Germany
- Gereon Reus, Fulda
- Thomas Möller, Fulda
- Jonas Jäger, Fulda
- Julian Hasenauer, Fulda
- Dr. Stewart T. Schultz, Zadar
- Dr. Claudia Kruschel, Zadar
- Dr. Viviane Wolff, Fulda
- Dr. Klaus Fricke-Neuderth, Fulda
The full paper Looking for Seagrass: Deep Learning for Visual Coverage Estimation (IEEE - OCEANS 2018 Kobe) is available at: HS Fulda
http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz
- Anaconda 3
- CUDA 8 & cuDNN 6
- VirtualEnv (Dependencies located in conda-env.yml)
- Install dependencies
- Create VirtualEnv
- Download InceptionNet V3 Graph
- Download LookingForSeagrass Dataset (and extract)
- Adapt three paths in experiment scripts
- Activate Virtual Env
- Run experiments
#Path to InceptionNetV3 ProtoBuf
GRAPH="/path/to/classify_image_graph_def.pb"
# Root Path of LookingForSeagrass Dataset
FOLDER_ROOT="/path/to/datasetroot/dataset"
# Path for storing your results
OUTPUT_PATH="/path/to/output/results"
Please cite our Paper:
@INPROCEEDINGS{8559302,
author={G. Reus and T. M{\"o}ller and J. J{\"a}ger and S. T. Schultz and C. Kruschel and J. Hasenauer and V. Wolff and K. Fricke-Neuderth},
booktitle={2018 OCEANS - MTS/IEEE Kobe Techno-Oceans (OTO)},
title={Looking for Seagrass: Deep Learning for Visual Coverage Estimation},
year={2018},
volume={},
number={},
pages={1-6},
keywords={},
doi={10.1109/OCEANSKOBE.2018.8559302},
ISSN={},
month={May},}