Time-unfolding Object Existence Detection
in Low-quality Underwater Videos using Convolutional Neural Networks
Helmut T
, Matthias Vahl
, Uwe Freiherr von Lukas
and Torsten Ullrich
Fraunhofer Institute for Computer Graphics Research IGD, Rostock, Germany
University of Rostock, Institute for Computer Science, Rostock, Germany
Fraunhofer Austria Research GmbH, Visual Computing Graz, Austria
Institute of Computer Graphics and Knowledge Visualization, Graz University of Technology, Austria
helmut.toedtmann@igd-r.fraunhofer.de, torsten.ullrich@fraunhofer.at
Convolutional Neural Network, Deep Learning, Environmental Monitoring, Implicit Segmentation, Detection.
Monitoring the environment for early recognition of changes is necessary for assessing the success of renat-
uration measures on a facts basis. It is also used in fisheries and livestock production for monitoring and for
quality assurance. The goal of the presented system is to count sea trouts annually over the course of several
months. Sea trouts are detected with underwater camera systems triggered by motion sensors. Such a scenario
generates many videos that have to be evaluated manually. This article describes the techniques used to auto-
mate the image evaluation process. An effective method has been developed to classify videos and determine
the times of occurrence of sea trouts, while significantly reducing the annotation effort. A convolutional neural
network has been trained via supervised learning. The underlying images are frame compositions automat-
ically extracted from videos on which sea trouts are to be detected. The accuracy of the resulting detection
system reaches values of up to 97.7 %.
It is of great interest to the world to keep na-
ture and environment in balance and to maintain a
healthy state. Based on the Red List of Endan-
gered Species (Winkler et al., 1991) the population
of sea trouts was seriously threatened. As a conse-
quence, in the time from from 1992 to 1999 renat-
uration programs attempted to repopulate these re-
gions as pilot project. The success of these programs
has been documented and guidelines were derived,
which serve as a basis for further population recovery
projects in other areas; the success of this project led
to similar projects in 30 further areas (Schwevers and
Adam, 2020). The measured results have been pub-
lished (Mannerla et al., 2011), but in 2015 difficulties
in correctly monitoring the populations have been dis-
covered (Pedersen et al., 2017), because the underly-
ing data was insufficient, erroneous and did not allow
to distinguish between different populations. Conse-
quently, a new method of regular counting was de-
veloped: Underwater video cameras were installed in
selected reference areas in order to precisely count all
incoming and outgoing sea trouts as shown in Fig-
ure 1. Sea trouts are a very special kind of fish. Once
every year, sea trouts leave the salt water of the Baltic
Sea and battle their way upstream into freshwater,
where they form hollows in sandy riverbeds to lay
their eggs. But at numerous spots along the way, their
path is blocked by dams or weirs. In many rivers, the
sea trouts have already been driven to extinction. Cur-
rent measuring methods are counting the hollows or
fishing a reference part of the water. All these meth-
ods are labor-intensive, locally limited and sometimes
dangerous to the stock. Environmental institutes in
Europe have been commissioned to monitor sea trout
numbers. Along the rivers, members of each institute
are constructing bottle necks that sea trout have to tra-
Figure 1: Camera setups in rivers are used to precisely count
sea trouts. The photo has been taken at a low water level.
verse and are monitoring them with video cameras.
But evaluating the images is still a work-intensive
process: an employee needs 1416 hours only to watch
over 340 000 videos that have been captured at all bot-
tle necks over the course of five months. An example
of the video frames to evaluate is shown in Figure 2.
It illustrates that even humans need training to detect
Figure 2: Video frames have to be evaluated manually or
automatically. The top row shows no sea trout whereas the
bottom row shows a positive result (sea trout).
and classify sea trout in video images.
This is where this article comes in. The new auto-
matic system described here takes a common personal
computer only five days to do the same work using a
machine learning approach. Another advantage of de-
ploying artificial intelligence (AI) is that it makes gen-
uine wide-scale surveillance of sea trout stocks pos-
sible, whereas previously only five or six rivers could
be monitored. Furthermore this approach allows to re-
place the usual costly video annotation process with a
fast and easy alternative, while obtaining the informa-
tion of temporal occurrence of sea trouts in a video.
The presented system uses artificial intelligence tech-
niques and applies them to a non-computer science
domain. In order to address both areas, computer
science and the field of application, the related work
from both areas will be examined.
2.1 Convolutional Neural Networks
An extensive survey on deep learning theory and ar-
chitectures is given by (Alom et al., 2019). The
authors motivate and explain the following concepts
which are crucial to understand the details of the re-
lated work and the approach: supervised learning,
convolutional neural network, pooling layer, activa-
tion function, optimization methods, transfer learn-
When choosing any deep learning approach for
the task of finding objects in images or videos, it is
important to distinguish between classification, detec-
tion and segmentation. Assuming that a video is sim-
ply a sequence of images, classification is the process
of deciding whether an image belongs to a particular
category among a set of given categories. Detection
attempts to find objects of a particular category within
an image and usually returns a list of bounding boxes.
A segmentation task can be understood as a more so-
phisticated detection on a per-pixel level. It returns a
binary mask, thus providing more precise results.
Krizhevsky, Sutskever, and Hinton introduced the
first convolutional neural network for classification
that achieved state of the art performance at the Im-
ageNet Large Scale Visual Recognition Challenge in
contrast to former used algorithms (Krizhevsky et al.,
2012). They used five convolutional layers, of which
some were followed by pooling layers, and addition-
ally three fully connected layers, while making use of
the rectified linear unit (ReLU) activation function. In
“Going Deeper with Convolutions” (Szegedy et al.,
2014) improved this approach with “GoogLeNet”
(see Figure 3). The designed network consisting of
22 layers, that needed twelve times less learned pa-
rameters, while still achieving a far better result. Con-
cerning detection, (Redmon et al., 2016) presented a
milestone, outperforming the former state of the art
of detection in terms of accuracy and speed, while
only needing one forward pass. They used 24 convo-
lutional layers followed by two fully connected lay-
ers. Instead of inception modules from “GoogLeNet”
they make use of simple (1 × 1) reduction layers and
(3 × 3) convolutional layers. In the field of segmen-
tation, (He et al., 2017) developed “Mask R-CNN”
which is based on the classification neural network
called “ResNet” presented by (He et al., 2016). The
network uses 101 layers. It consists of three main
components, which compute the bounding boxes, the
class identity, and the segmentation mask.
2.2 Fishes in Computer Vision
Deformable template matching can be used to clas-
sify two species of fish (Rova et al., 2007). The
authors improved over previous Support Vector Ma-
chine (SVM)-based methods and reached an accuracy
of up to 90%. However, the approach required manu-
ally cropped underwater video images.
Spampinato et al. introduced a framework
for detecting, tracking, and counting fishes in
Figure 3: Structure of original “GoogLeNet” has been designed by (Szegedy et al., 2014).
videos (Spampinato et al., 2008). A classification ac-
curacy of 93% is achieved while the overall count-
ing success rate is about 85%. Their approach only
worked on video data of controlled environments and
required extensive human annotation beforehand.
(Ravanbakhsh et al., 2015) detect fishes in under-
water video with a shape-based approach. A Haar
classifier is used for precise localisation of fish head
and snout, effectively leading to a sub pixel accuracy
for retaining the shape of the fish. For a set of 35 sam-
ples, they report an accuracy of up to 100%. However,
this approach seems not to work in uncontrolled envi-
ronments and requires images that contain completely
visible fish.
An approach using convolutional neural networks
has been presented by (French et al., 2015). They
make us of a segmentation approach based on the
fields” algorithm and tested a variety of differ-
ent architectures of which the best delivered a count
accuracy of 92,87% (relative error of 7,13%). Unfor-
tunately, this approach requires segmentation annota-
tion for each video frame of the training set.
(Shafait et al., 2016) introduce image set classi-
fication with a one-nearest-neighbour classification.
Given a set of already localised images of a fish with
unknown species type, it is matched with all exist-
ing training sets; for which multiple exist for each
class. Instead of calculating an average pairwise dis-
tance between all possible image combinations, they
construct synthetic images as linear combination from
previously calculated base images to be as similar as
possible. The label of the training image set with the
smallest distance is applied. They achieve an accu-
racy of 94,6%.
In “Comparison between Deep Learning and
HOG+SVM Methods” (Villon et al., 2016) compared
the combination of histogram of oriented gradients
(HOG) and support-vector machines (SVM) against
a customized GoogleNet architecture for detecting
coral reef fishes. Training was done with labeled
cropped objects from video frames, while testing
was done with a sliding window approach on video
frames. It was found that the deep learning approach
performed better in general.
(Li et al., 2016) use a Fast RCNN approach on
images of fish to distinguish between 12 classes and
reach a mean average precision of 81.4%. How-
ever, the approach works only on images and requires
bounding box annotation for each video frame of the
training set.
An approach by (Sung et al., 2017) uses the “You
Only Look Once” (YOLO) algorithm (Redmon et al.,
2016) to detect fishes on underwater images. A grid
search on the hyper parameters is performed to im-
prove the performance. Also, their data set is en-
hanced with lots of positive and negative samples to
create a more robust network and reach a classifica-
tion accuracy of 93%.
In “Fish Recognition Using Convolutional Neu-
ral Network” the authors handcrafted several convolu-
tional neural network architectures to perform a clas-
sification task on four species of fish (Ding et al.,
2017). The best network achieved a classification ac-
curacy of 96,5%.
Further improvements have been obtained
by (Rathi et al., 2018). They also develop a novel
convolutional neural network architecture that trains
on denoised still images from a custom fish data
set and includes a threshold-based segmentation
as additional fourth image layer. The result is an
classification accuracy of 96,29%.
An approach working on an input data set with a
video quality comparable to the presented system has
been presented by (Shevchenko et al., 2018). They
apply three background subtraction methods on un-
derwater videos to detect fishes. The best method
achieves 60% accuracy.
2.3 Summary
While the field of deep learning for object recogni-
tion undergoes constant improvements, the domain of
dealing with fishes in underwater images and videos
has not yet been making use of all the newly de-
veloped possibilities to the full extent. Contrary to
our needs, some of these approaches additionally deal
with tracking, shape retrieval and species classifica-
tion, while most of them are restricted to images or
even selected parts of images. Our use case of binary
video classification with obtaining the times of occur-
rence of sea trout on vast amount of video data is not
present in the available literature. To the best of the
authors’ knowledge the following approach has not
been proposed before.
The used camera system has been selected in the pre-
vious research project so that no further intervention
was possible. For the sake of completeness, the origi-
nal video material is described here.
3.1 Data Set Description
Figure 4: Selection of videos containing sea trout. The
frame containing the most fish like features was selected.
The installed camera systems recorded 341 238
videos in total. The videos show eight different rivers
over the course of five months. Each video has a
resolution of 704 × 576 pixels and a frame rate of
15 frames per second. Figure 4 and Figure 5 show-
case visual variance, low quality and separability dif-
ficulty. The first 20 lines of each frame are reserved
for overlaid video information and thus removed in
the preparation process, leading to an effective reso-
lution of 704 × 556 pixels. The video length varies
between 5 and 300 seconds, the average duration of a
video is about 15 seconds. Videos are stored as a Ma-
troska Video (MKV) files, using the h264 codec. The
average size of a video file is approximately 550 kilo-
bytes. For generating labeled learning material 10 %
of these videos were selected at equal time intervals
and manually classified by the domain expert Uwe
Friedrich with the tools described in the Section “Im-
plementation”. As a consequence, 307 114 videos re-
mained unlabeled.
Figure 5: Selection of videos containing no sea trout. The
frame containing the most fish like features was selected.
3.2 Detection by Classification
As mentioned in the Section “Related Work”, almost
all approaches of detecting objects in still images and
videos require labeled data, for example bounding
boxes or polygons marking these objects. For videos
this labeling process is very time consuming, since a
sufficient number of frames have to be annotated for
each video. In our use case, any of these approaches
were not usable, since the amount of available video
data and the visual variance was too high to create
labeled data for supervised learning. Instead, we de-
veloped an approach that only requires the informa-
tion of whether a video contains sea trouts (see Fig-
ure 6). This approach can reliably detect the existence
Figure 6: The pipeline starts with a video and returns a bi-
nary classification.
of sea trout in previously unseen videos and beyond
that, can deliver information on which frames of a
video contained sea trout, and potentially even where
in these frames the sea trout was located. For each
single video the workflow is as follows:
1. removing video header
2. time-unfolding convolution
3. implicit coarse segmentation
4. 2D classification
3.3 Time-unfolding Convolution
In order to enable classification on videos, a sufficient
number of individual images from a video sequence
are arranged next to each other in a grid layout. Fig-
ure 7 shows an example with 64 successive images of
a video.
Figure 7: The image sequence of a video is arranged in a
2D layout.
Convolutional neural networks are “a natural way
to embed translation invariance” (Deng et al., 2013),
i.e. for the recognition of learned features it is irrele-
vant where these are located in the image, as long as
they are present. That way, existing pre-trained neural
networks for 2D classification can be used with little
modifications as opposed to developing and training
a 3D classification network from scratch. We found
that resulting artificial edges between images did not
hurt the overall classification performance, as these
occur both in image grids containing and not contain-
ing sea trout and thus did not represent an important
visual feature. On the one hand there must be enough
single images to reliably detect even a short appear-
ance of a sea trout in a video. On the other hand not
any arbitrarily high number of single images can be
used for calculation. In detail, small images would
negate possible information retrieval, but big image
grids would represent a limiting factor concerning the
graphics memory and the general performance of the
graphics card. An 8 × 8 grid has proven to be the per-
fect trade-off. In our tests a 4 × 4 grid often didn’t
capture short occurrences of fish, while a 16 ×16 grid
implicated images which have been too small for de-
tection. A video with width w, height h, and a scale
value s, leads to s
evenly distributed sample frames;
i.e. the three dimensional information is rearranged in
two dimensions as follows:
f (x, y,t) 7→
x + w · (t mod s)
y + h · bt/sc
t [0,s
1],x [0,w 1], y [0,h 1]
Using this scaled grid the resulting image has the
same dimensions as a single frame from the original
video (see Figure 7).
3.4 Implicit Coarse Segmentation
The used network structure is a modified and pre-
trained “GoogLeNet” (see Figure 3 and (Szegedy
et al., 2014)). We chose this architecture due to its
good performance, which has been confirmed in our
tests, while being fast. The input layer has been
changed to match the dimension of the image grid.
Consequently, the sizes of all following layers are
increased, leading to a final inception layer with di-
mension 22 × 17 and the corresponding final pool-
ing layer of dimension 16 × 11. In that regard the
step of increasing the size of the input layer to match
the image size is technically equivalent to removing
pooling layers with or without their connected convo-
lutional layers deeper in the network, like shown in
(Zhou et al., 2016). This shifts the category of the
algorithm from simple classification to implicit seg-
mentation with very low resolution, neither requiring
segmented training data, nor delivering segmented re-
sults. The output of the final inception layer can be
interpreted as set of activation maps, indicating which
parts of the grid image are responsible for the classi-
fication result. For an activation map A with width
w and height h, the time point t of the highest ac-
tivation can be found as follows: Within the range
R = [0,w 1] × [0,h 1] the maximum (x,y) meets
the equation
(x,y) R (u, v) R : A(u, v) A(x,y).
Using the scaling factor s mentioned above the time t
can be determined via
t =
y · s
x · s
It is important to note that the highest activation of
the implicit coarse segmentation – the final value of t
– is not directly used for the classification. It is rather
an evaluation tool to ensure that the neural network
indeed learned a set of useful features to detect sea
trouts in the grid image. For example, Figure 8 is in-
dicating that the most important parts of the grid im-
age from Figure 7 are at t = 7 (first row to the right)
and t = 8 (second row to the left), which clearly con-
tain sea trout. The whole second row of the grid im-
age also contains sea trout, but is not highly visually
present in this activation map, which may indicate that
either these frames are not necessary for the network
to detect sea trout, or are present in another activation
Figure 8: This visualisation shows the activation map that
corresponds to Figure 7. The regions with a high activation
(red) contain sea trouts, which can be verified in the input
images shown in the grid layout.
In order to realize the new approach several tools have
been developed. The software has been programmed
using the integrated development environment “Qt”
(www.qt.io) in the programming language C++. The
software relies on the NVIDIA fork of the framework
Caffe (Jia et al., 2014) using GPU acceleration. As a
consequence, at least a CUDA 8.0 compatible graph-
ics card has to be present, including the appropriate
drivers and libraries.
4.1 Annotation Tool
The annotation tool loads a custom set of videos, dis-
plays them in a list, while the currently selected video
is played in a loop. By a single key stroke the anno-
tator can decide if that video contains sea trout or not,
after which the next video is selected. With this tool,
an experienced annotator needs just a few seconds to
label a video. Having completed the annotation of the
complete set of videos, they are copied and split to
separate folders according to their class.
4.2 Preparation Tool
From a set of videos, in each video the header is re-
moved to prevent the learning of misleading informa-
tion or introducing biases. Then the time-unfolding
convolution is performed. The resulting grid images
are stored as lossless compressed Portable Network
Graphics (PNG) files.
4.3 Training the Network
During the training of the network the deep learn-
ing framework DIGITS (developer.nvidia.com/digits)
is used. First, a data set for classification is cre-
ated out of the grid images from the preparation tool.
Half of the data is used for training, the other half
is reserved for validation and testing. According to
the number of different scenarios, class balancing
is performed to assure each scenario is represented
with about the same number of grid images. Like-
wise, class balancing is performed again with fo-
cus on the ratio of sea trout videos to non-sea trout
videos. Class balancing is done with enriching the
smaller class/scenario with duplicates of itself. The
Adam optimizer (Kingma and J., 2015) is used with
the learning rate lr = 0.0001 and the exponential de-
cay γ = 0.95. The training of the network took about
75 hours on four simultaneously used GPUs of type
GeForce GTX TITAN X with 12 GB of RAM each.
4.4 Analysis Tool
The trained network is loaded into the Caffe frame-
work for C++. Afterwards, a custom set of unlabeled
videos is loaded. The steps from the preparation tool
are performed. For each resulting grid image, infer-
ence is performed with the convolutional neural net-
work. The corresponding videos are copied and split
into separate folders according to the classification re-
sult. Finally, a report is generated, listing all videos
with their assigned class and classification confidence
in the form of the softmax function.
The trained network has been evaluated with unla-
beled videos. The data set (see Section “Data Set De-
scription”) comprehends 307 114 unlabeled videos.
The analysis tool has classified 5 235 of 307 114
videos as “sea trout”. A manual control by the do-
main expert Uwe Friedrich confirmed that 5 098 of
5 235 videos were containing sea trout, resulting in
a precision of 97.38%. Likewise a showcase sam-
ple of 6 000 videos from the 307 114 videos has been
selected and then classified by both the expert and
the analysis tool. The expert classified 938 of these
videos as “sea trout”, while the analysis tool classi-
fied 902 videos as “sea trout”, resulting in a precision
of 96.16 %. The results are listed in Table 1. For the
first case, no accuracy can be reported due to the lack
of ground truth data, for the second case, an accuracy
Table 1: Evaluation of the new system using “previously
unseen” data.
Test Positives Sea trout
Size by System by Expert Precision
307 114 5 235 5 098 97.38%
6 000 938 902 96.16%
of 97.70 % is achieved:
accuracy =
(T P + T N)
(T P + FP + FN + T N)
= 97.70 %
The values of true/false positive/negative detections
are listed in Table 2.
Table 2: Confusion matrix for selected set. Abbreviations
are for true/false positive/negative.
Labeled Labeled
6 000 Samples positive negative
Detected true T P = 902 FP = 102
Detected f alse FN = 36 T N = 4 960
Demonstrated at sea trouts examples, a convolutional
neural network has been trained via supervised learn-
ing. The underlying images are frame compositions
automatically extracted from videos on which sea
trouts are to be detected. The approach detects ob-
jects in underwater videos even in use cases with re-
duced quality: low resolution, minimal contrast, poor
6.1 Contribution
It was shown that time-unfolding convolution enables
the use of still image classification rather than the
usual detection or segmentation approaches, which in
turn allows much faster annotation of ground truth
data while also achieving very high accuracy. With
the use of implicit segmentation, further statements
can be made about the times of the occurrence of sea
trout in the video. The accuracy of the resulting de-
tection system reaches values of up to 97.7 %.
6.2 Benefit
The manual evaluation of videos does not scale.
Within this project an employee would need 1416
hours only to watch all videos that have been cap-
tured. The automatic solution is not only much faster,
since no interaction is necessary, the time required
even becomes irrelevant. Now, the monitoring pro-
cess scales and is not the limiting factor any more.
6.3 Outlook
In the future, network structure modifications to in-
crease the implicit segmentation resolution will be ex-
plored, which could allow implicit detection of loca-
tions of sea trout features in a video frame. Likewise,
we will evaluate how low the quality of the videos can
be and what the optimal parameters for the time un-
folding convolution are. Furthermore it is imaginable
to improve on the process of class activation mapping
and develop general purpose approaches for precisely
detecting and segmenting arbitrary objects in videos
with little to no information about position and times
of occurrence.
The authors would like to thank Uwe Friedrich from
the Institut f
ur Fisch und Umwelt. All the data was
generated and labeled in the project “Saisonale Er-
mittlung des Meerforellenbestandes in den Einzugs-
gebieten der Vorpommerschen Boddengewsser und
der Mecklenburger Bucht mittels videooptischer Er-
fassung aufsteigender Individuen in Verbindung mit
Kartierungsarbeiten und fischereibiologischen Unter-
suchungen”, project number DRM-149, “Landes-
forschungsanstalt fr Landwirtschaft und Fischerei ”.
The authors also thank Tim Dolereit, Tom Krause
and Mohamad Albadawi from MAG of Fraunhofer
Institute for Computer Graphics Research in Rostock
for their valueable input.
Furthermore, the authors acknowledge the gen-
erous support of the Carinthian Government and
the City of Klagenfurt within the innovation center
Alom, M. Z., Taha, T. M., Yakopcic, C., Westberg, S.,
Sidike, P., Nasrin, M. S., Hasan, M., Van Essen, B. C.,
Awwal, A. A. S., and Asari, V. K. (2019). A State-of-
the-Art Survey on Deep Learning Theory and Archi-
tectures. Electronics, 8:292ff.
Deng, L., Abdel-Hamid, O., and Yu, D. (2013). A Deep
Convolutional Neural Network using Heterogeneous
Pooling for Trading Acoustic Invariance with Pho-
netic Confusion. IEEE International Conference on
Acoustics, Speech, and Signal Processing (ICASSP),
Ding, G., Song, Y., Guo, J., Feng, C., Li, G., and He, B.
(2017). Fish Recognition Using Convolutional Neural
Network. Mts, pages 0–3.
French, G., Fisher, M., Mackiewicz, M., and Needle, C.
(2015). Convolutional Neural Networks for Counting
Fish in Fisheries Surveillance Video. Machine Vision
of Animals and their Behaviour Workshop, 7:1–10.
He, K., Gkioxari, G., Dollar, P., and Girshick, R. (2017).
Mask R-CNN. IEEE International Conference on
Computer Vision (ICCV), 15:2980–2988.
He, K., Zhang, X., Ren, S., and Sun, J. (2016). Deep
Residual Learning for Image Recognition. IEEE Con-
ference on Computer Vision and Pattern Recognition
(CVPR), 19:770–778.
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J.,
Girshick, R., Guadarrama, S., and Darrell, T. (2014).
Caffe: Convolutional Architecture for Fast Feature
Embedding. International Conference on Multimedia,
Kingma, D. P. and J., B. L. (2015). Adam: A Method for
Stochastic Optimization. International Conference on
Learning Representations (ICLR), 3:19ff.
Krizhevsky, A., Sutskever, I., and Hinton, G. E. (2012). Im-
agenet classification with deep convolutional neural
networks. International Conference on Neural Infor-
mation Processing Systems, 25:1097–1105.
Li, X., Shang, M., Qin, H., and Chen, L. (2016). Fast accu-
rate fish detection and recognition of underwater im-
ages with Fast R-CNN. OCEANS 2015 - MTS/IEEE
Washington, pages 1–5.
Mannerla, M., Andersson, M., Birzaks, J., Debowski, P.,
Degerman, E., Huhmarniemi, A., H
om, H.,
Ikonen, E., Jokikokko, E., Jutila, E., Kesler, M.,
Kesminas, V., Kontautas, A., Pedersen, S., Persson, J.,
Romakkaniemi, A., Saura, A., Shibaev, S., Titov, S.,
Tuus, H., Tylik, K., and Yrj
a, T. (2011). Salmon and
Sea Trout Populations and Rivers in the Baltic Sea.
Helsinki Commission, 1 edition.
Pedersen, S., Degerman, E., Debowski, P., and Petereit, C.
(2017). Assessment and recruitment status of Baltic
Sea trout populations. Sea Trout: Science & Man-
agement: Proceedings of the International Sea Trout
Symposium, 2:423–441.
Rathi, D., Jain, S., and Indu, S. (2018). Underwater
Fish Species Classification using Convolutional Neu-
ral Network and Deep Learning. International Con-
ference on Advances in Pattern Recognition (ICAPR),
Ravanbakhsh, M., Shortis, M. R., Shafait, F., Mian, A., Har-
vey, E. S., and Seager, J. W. (2015). Automated Fish
Detection in Underwater Images using Shape-based
Level Sets. Photogrammetric Record, 30:46–62.
Redmon, J., Divvala, S., Girshick, R., and Farhadi, A.
(2016). You Only Look Once: Unified, Real-Time
Object Detection. IEEE Conference on Computer Vi-
sion and Pattern Recognition (CVPR), 19:779–788.
Rova, A., Mori, G., and Dill, L. M. (2007). One fish, two
fish, butterfish, trumpeter: Recognizing fish in under-
water video. IAPR Conference on Machine Vision Ap-
plications (MVA), 7:404–407.
Schwevers, U. and Adam, B. (2020). Fish Protection Tech-
nologies and Fish Ways for Downstream Migration.
Springer International Publishing, 1 edition.
Shafait, F., Mian, A., Shortis, M., Ghanem, B., Culver-
house, P. F., Edgington, D., Cline, D., Ravanbakhsh,
M., Seager, J., and Harvey, E. S. (2016). Fish Iden-
tification from Videos Captured in Uncontrolled Un-
derwater Environments. ICES Journal of Marine Sci-
ence: Journal du Conseil, 73:2737–2746.
Shevchenko, V., Eerola, T., and Kaarna, A. (2018). Fish
Detection from Low Visibility Underwater Videos.
International Conference on Pattern Recognition,
Spampinato, C., Chen-Burger, Y. H., Nadarajan, G., and
Fisher, R. B. (2008). Detecting, Tracking and Count-
ing Fish in Low Quality Unconstrained Underwater
Videos. International Conference on Computer Vision
Theory and Applications (VISAPP), 3:514–519.
Sung, M., Yu, S.-C., and Girdhar, Y. (2017). Vision based
Real-time Fish Detection Using Convolutional Neural
Network. OCEANS, 7:1–6.
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S.,
Anguelov, D., Erhan, D., Vanhoucke, V., and Rabi-
novich, A. (2014). Going Deeper with Convolutions.
IEEE Conference on Computer Vision and Pattern
Recognition (CVPR), 17:1–9.
Villon, S., Chaumont, M., Subsol, G., Vill
eger, S., Claverie,
T., and Mouillot, D. (2016). Coral Reef Fish Detec-
tion and Recognition in Underwater Videos by Super-
vised Machine Learning: Comparison between Deep
Learning and HOG+SVM Methods. Lecture Notes in
Computer Science, Artificial Intelligence, and Bioin-
formatics, 10016:160–171.
Winkler, H. M., Hamann, N., and Waterstraat, A. (1991).
Rote Liste der gef
ahrdeten Rundm
auler, S
und Wanderfischarten Mecklenburg-Vorpommerns.
Umweltministerium Mecklenburg-Vorpommern, 1
Zhou, B., Khosla, A., A., L., Oliva, A., and Torralba, A.
(2016). Learning Deep Features for Discriminative
Localization. CVPR.