Mixing Augmentation and Knowledge-Based Techniques in Unsupervised
Domain Adaptation for Segmentation of Edible Insect States
Paweł Majewski
1 a
, Piotr Lampa
2 b
, Robert Burduk
1 c
and Jacek Reiner
2 d
1
Faculty of Information and Communication Technology, Wrocław University of Science and Technology, Poland
2
Faculty of Mechanical Engineering, Wrocław University of Science and Technology, Poland
Keywords:
Augmentation, Domain Adaptation, Instance Segmentation, Edible Insects, Tenebrio Molitor.
Abstract:
Models for detecting edible insect states (live larvae, dead larvae, pupae) are a crucial component of large-scale
edible insect monitoring systems. The problem of changing the nature of the data (domain shift) that occurs
when implementing the system to new conditions results in a reduction in the effectiveness of previously
developed models. Proposing methods for the unsupervised adaptation of models is necessary to reduce the
adaptation time of the entire system to new breeding conditions. The study acquired images from three data
sources characterized by different types of cameras and illumination and checked the inference quality of
the model trained in the source domain on samples from the target domain. A hybrid approach based on
mixing augmentation and knowledge-based techniques was proposed to adapt the model. The first stage of the
proposed method based on object augmentation and synthetic image generation enabled an increase in average
AP
50
from 58.4 to 62.9. The second stage of the proposed method, based on knowledge-based filtering of target
domain objects and synthetic image generation, enabled a further increase in average AP
50
from 62.9 to 71.8.
The strategy of mixing objects from the source domain and the target domain (AP
50
=71.8) when generating
synthetic images proved to be much better than the strategy of using only objects from the target domain
(AP
50
=65.5). The results show the great importance of augmentation and a priori knowledge when adapting
models to a new domain.
1 INTRODUCTION
Edible insects are one of the most promising alterna-
tive sources of novel food. The number of large-scale
edible insect farms is increasing yearly due to the pos-
sibility of obtaining a high-protein product at a rela-
tively low-cost (Dobermann et al., 2017). Edible in-
sect breeding is a good solution for utilizing unused
areas of livestock buildings where animal diseases
such as ASF (African swine fever) previously oc-
curred (Thrastardottir et al., 2021). The need to mea-
sure breeding parameters and detect anomalies, com-
bined with the large-scale nature of breeding, necessi-
tates using a dedicated automated monitoring system.
There have recently been few works regarding
monitoring edible insect breeding related to the Tene-
brio Molitor. (Majewski et al., 2022) proposed a
multi-purpose 3-module system, enabling the detec-
a
https://orcid.org/0000-0001-5076-9107
b
https://orcid.org/0000-0001-8009-6628
c
https://orcid.org/0000-0002-3506-6611
d
https://orcid.org/0000-0003-1662-9762
tion of edible insect growth stages and anomalies
(dead larvae, pests), semantic segmentation of feed,
chitin, and frass, and larvae phenotyping. The authors
used synthetic images generated from a pool of ob-
jects, significantly reducing model development time.
Other works were based on solutions dedicated to sin-
gle issues, e.g. classification of larvae segments (Baur
et al., 2022), and classification of the gender of pupae
(Sumriddetchkajorn et al., 2015). Undoubtedly, the
results presented in this works demonstrate the fea-
sibility of using methods based on machine learning
and computer vision to inspect edible insect breeding
effectively. However, adapting the developed meth-
ods to new breeding conditions is still an open prob-
lem.
In the literature, we can find a significant num-
ber of unsupervised model adaptation methods for
the problems of image classification (Madadi et al.,
2020), semantic segmentation (Toldo et al., 2020), or
object detection (Oza et al., 2021); however, there
are fewer works in the area of instance segmenta-
tion. Among the most important domain adapta-
380
Majewski, P., Lampa, P., Burduk, R. and Reiner, J.
Mixing Augmentation and Knowledge-Based Techniques in Unsupervised Domain Adaptation for Segmentation of Edible Insect States.
DOI: 10.5220/0011603500003417
In Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 5: VISAPP, pages
380-387
ISBN: 978-989-758-634-7; ISSN: 2184-4321
Copyright
c
2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)
tion methods are discrepancy-based (Csurka et al.,
2017; Saito et al., 2018), adversarial-based (includ-
ing generative-based) (Yoo et al., 2016; Murez et al.,
2018), reconstruction-based (including graph-based)
(Cai et al., 2019) and self-supervision-based (Khod-
abandeh et al., 2019; Shin et al., 2020). A rela-
tively simple and intuitive approach to domain adap-
tation is pseudo-label-based self-training, which in-
volves training the model for the target domain based
on samples with pseudo-labels representing a predic-
tion of the model trained on labelled samples from
the source domain. An important element in this ap-
proach is prediction filtering.
The pseudo-label-based self-training approach
seems suitable for instance segmentation and even
easier to apply than in object detection. Namely, hav-
ing masks for objects, it is possible to extract them
from images, add them to appropriate object pools
and use them further to generate synthetic images. It
is also easier to perform filtering at the object level, as
it is possible to calculate features for a specific object.
This work proposed a two-stage hybrid method for
domain adaptation based on using pseudo-labels for
self-training. In 1st stage, it was proposed to expand
the training set of samples through augmentation at
the image and object levels to reduce the overfitting
of the model on the source domain. In 2nd stage, fil-
tering of the obtained predictions was carried out us-
ing domain knowledge. An essential contribution of
this work is the study of the importance of creating
the training set in the 1st and 2nd stages, especially
the concept of mixing real and synthetic samples and
mixing samples from the source domain (with real
labels) and the target domain (with pseudo-labels).
In addition, the consequences of using only synthetic
data (no real labelled samples in the training set) on
the model’s performance in cases of inference in and
out of the domain were also examined.
2 MATERIAL AND METHODS
2.1 Problem Definition
The problem addressed is detection and segmentation
from images of three states of edible insects, namely
(1) live larvae, (2) dead larvae, and (3) pupae. The
samples are in the form of 512x512 images and come
from three sources associated with different types of
recording cameras and lighting, namely (1) CA, (2)
LU, and (3) JA. Examples of samples from the consid-
ered sources, along with the type of objects detected,
are shown in Figure 1.
Figure 1: Examples of samples from the considered
sources: (a) RGB images, (b) types of detected objects.
The main objective of the research was to pro-
pose a suitable domain adaptation method to train the
model on one data source (source domain) with la-
belled samples and make inference on another (target
domain) with unlabelled samples with relatively low
error. The proposed method is expected to reduce the
destructive effect of domain shift on the accuracy of
target domain prediction.
2.2 Data Sources
The samples were acquired using three image acqui-
sition systems, differing in the cameras and lighting
used. The first one (CA) was an experimental sta-
tion with a EOS 50D camera (Canon, Tokyo, Japan)
with a resolution of 5184 x 3456 pixels and a zoom
lens. Diffuse white fluorescence lighting was used.
The second (LU) was a data acquisition station pur-
posely built for imaging insects in breeding boxes. It
used a Phoenix PHX120S-CC (LUCID Vision Labs,
Richmond, Canada) camera with a resolution of 4096
x 3000 pixels and a 12 mm focal length lens. Samples
were illuminated with neutral white LEDs in a diffu-
sion tunnel. The third (JA) was a machine vision sys-
tem prepared for industrial implementation for Tene-
brio Molitor breeding. A GOX-12401C-PGE (JAI,
Copenhagen, Denmark) camera was used, with a res-
olution of 4096 x 3000 pixels and a 12 mm lens. In
this case, due to size limitations, LED strips providing
cold white light were used for direct illumination.
2.3 Dataset
A dataset was prepared for the study, containing sam-
ples from various defined sources along with marked
object masks from the defined classes. A total of 15
samples from CA, 29 samples from LU and 36 sam-
Mixing Augmentation and Knowledge-Based Techniques in Unsupervised Domain Adaptation for Segmentation of Edible Insect States
381
ples from JA were labelled. A summary of the la-
belled number of objects can be found in Table 1.
Table 1: The number of objects from defined classes in the
considered image sources.
source type object type no. of objects
CA
live larvae 656
dead larvae 250
pupae 124
LU
live larvae 163
dead larvae 55
pupae 83
JA
live larvae 1247
dead larvae 148
pupae 187
2.4 Data Exploration
For initial data exploration and qualitative evaluation
of domain shift, PCA and visualization of selected
components were performed. The FID (Frechet In-
ception Distance) metric (Heusel et al., 2017) was
also calculated as a measure of the similarity of fea-
tures extracted from images belonging to different
sources. Lower values of the FID metric mean higher
similarity of sample distributions. FID and PCA were
based on a feature vector of length 2048 extracted
from the last pooling layer of the deep convolutional
neural network Inceptionv3 (Szegedy et al., 2015).
Masked images of objects (without surrounding back-
ground) were used for feature extraction.
2.5 Domain Adaptation with Mixing
Augmentation and
Knowledge-Based Techniques
The proposed adaptation method consists of two
stages described in detail in the following sections.
The first stage is based on the augmentation of source
domain objects and the generation of synthetic im-
ages. The second stage considers filtering target ob-
jects based on domain knowledge and re-generating
synthetic images using new target domain objects.
The idea scheme of the proposed solution is shown
in Figure 2.
The method for generating synthetic images in-
volved randomly placing objects on the background
image and allowing the simulation of object overlap
in dense scenes. Each generated synthetic image was
associated with an automatically generated label. The
method of generating synthetic images is described
in more detail in (Majewski et al., 2022; Toda et al.,
2020).
Figure 2: Idea scheme of the proposed solution detailing
two stages.
2.5.1 First Stage of Approach with Objects
Augmentation
The basis for training models is a set of real labelled
samples from the source domain. Evaluation results
for a model trained only on a set of real samples (the
only_real method) were used as a reference for the
following proposed approaches.
Object-level augmentation and synthetic image
generation were proposed to extend the source do-
main samples distribution. First, individual objects
were extracted from real images. Then, these objects
were augmented, modifying colour, contrast, sharp-
ness and brightness. The generated objects were then
placed on the background image, obtaining automati-
VISAPP 2023 - 18th International Conference on Computer Vision Theory and Applications
382
cally labelled synthetic images. Examples of the aug-
mented objects and the generated synthetic images are
shown in Figure 2.
Three possibilities for constructing the training
set for 1st stage were identified. The only_real
method assumes training only on real data, the
real_synthetic method - on real and synthetic data,
and the only_synthetic method - only on synthetic
data. For each setting, the Mask-RCNN (He et al.,
2017) with backbone ResNet-50 (He et al., 2016)
model was trained with default training parameters.
An implementation of the Mask R-CNN model from
the detectron2 (Wu et al., 2019) library was used in
the study. The part related to the 1st stage in Figure 2
shows the real_synthetic approach for creating the
training set.
2.5.2 Second Stage of Approach with
Knowledge-Based Filtering
The first component of 2nd stage of the proposed so-
lution is an inference using the model trained in 1st
stage on unlabelled target domain samples. The re-
sulting predictions were treated as pseudo-labels that
needed to be filtered to remove false positive predic-
tions. For filtering, it was used a priori domain invari-
ant knowledge, namely: (1) live larvae are the major-
ity class (see in Table 1), (2) objects from the classes
live larvae and dead larvae are the longest (have the
largest dimension of the longer side of the bound-
ing box), (3) objects from the dead larvae class have
the lowest pixel intensity, (4) objects from the pupae
class have the highest pixel intensity. The proposed
knowledge-based filtering assumes successively:
1. selection of objects with a minimum length of the
longer side of the bounding box d
min
, with a pre-
dicted class live larvae,
2. removal of outliers including mean intensity, size,
and length of the longer side of the bounding box
among the objects extracted in 1st step, obtaining
a distribution of samples representing live larvae,
3. calculation of intensity limits x
min
, x
max
for sam-
ples representing live larvae,
4. selection of objects with intensity values greater
than x
max
, with predicted class pupae,
5. selection of objects with intensity values less than
x
min
, with predicted class larvae dead.
6. removal of outliers among the objects extracted in
the 4th and 5th steps, obtaining a distribution of
samples representing pupae and dead larvae.
The obtained new samples in the form of target
domain objects and new generated objects after aug-
mentation are then used to generate synthetic data.
In 2nd stage, we have available the following sam-
ple distributions: (1) real from the source domain,
(2) synthetic from the source domain, (3) synthetic
from the target domain. The study investigated the
following training strategies: the "only target domain
samples" strategy assumes training the model only
on synthetic data from the target domain, and the
"mixed source/target domain samples" strategy as-
sumes mixing samples from the source domain and
target domain in the training set. Considering the
"mixed source/target domain samples" strategy, in
all the approaches identified in 1st stage (only_real,
real_synthetic, only_synthetic), the training set de-
fined in 1st stage is extended with synthetic sam-
ples from the target domain. Figure 2 shows the
"mixed source/target domain samples" strategy with
the real_synthetic variant.
2.6 Evaluation
The proposed methods were evaluated using the av-
erage precision AP
50
metric, a standard metric for the
evaluation in object detection tasks. The value of the
AP
50
metric represents the area under the precision-
recall curve after appropriately interpolating the chart
fragments. The AP
50
metric assumes a threshold value
of intersection over union (IoU) 50% between the true
and predicted bounding box to consider the prediction
significant. Details regarding the calculation of the
AP
50
metric can be found in (Majewski et al., 2022;
Padilla et al., 2020).
For the study, 6 possible cases of out-domain
crossing (source domain → target domain) were
selected, namely CA → LU, CA → JA, LU → CA,
LU → JA, JA → CA, JA → LU. Evaluation for the
out-domain inference cases was carried out for all
samples from the target domain. The AP
50
values for
in-domain inference were also determined as a refer-
ence. For the in-domain case, the entire dataset was
divided into training data (80% of samples) and test
data. Evaluation was performed on the test set.
3 RESULTS AND DISCUSSION
As part of the data exploration, PCA was performed,
and FID metrics were calculated between sample dis-
tributions. A visualization of the selected components
for samples from all data sources and defined classes
can be found in Figure 3. The calculated FID values
can be found in Table 2.
Based on the results from Figure 3 and Table 2,
it can be seen that objects from the live larvae class
are most similar to each other between distributions.
Mixing Augmentation and Knowledge-Based Techniques in Unsupervised Domain Adaptation for Segmentation of Edible Insect States
383
Figure 3: Selected principal components for domain shift
exploration based on deep features (Inceptionv3).
Table 2: Comparison of calculated FID metrics between
sources based on real samples.
sources object type FID
CA and LU
live larvae 124
dead larvae 166
pupae 144
all (average) 145
CA and JA
live larvae 69
dead larvae 110
pupae 113
all (average) 97
LU and JA
live larvae 97
dead larvae 120
pupae 115
all (average) 111
The FID distances between (CA and JA) and (LU and
JA) distributions are smaller than the distance be-
tween (CA and LU) distributions, which is also con-
firmed by Figure 3. For the selected components
(PC3 and PC4), samples from JA (blue markers) are
between samples from CA (red) and LU (green).
A comparison of different domain adaptation
methods can be found in Table 3 for the "mixed
source/target domain samples" strategy and in Table 4
for the "only target domain samples" strategy. As
reference values for assessing the quality of domain
adaptation are the results obtained by the models
trained and tested in-domain presented in Table 5.
The results obtained for 1st stage of model adap-
tation (Table 3) prove that the real_synthetic method
(average AP
50
= 62.9), which assumes the use of both
real and synthetic samples for training, is the most
suitable for use in the problem under consideration.
The use of only synthetic samples (only_synthetic
method, average AP
50
= 54.4) or only real samples
(only_real method, average AP
50
= 58.4) may be bet-
ter in special cases (only_synthetic for LU → CA,
only_real for LU → JA, CA → JA), but in gen-
eral (averaged), these approaches achieve smaller
AP values than the real_synthetic method. For
the special cases mentioned above, the differ-
ence between the best-obtained result and the AP
value for the real_synthetic method did not exceed
∆AP
50
= 4.0. On the other hand, for the LU → CA,
the difference between the AP values for only_real
and real_synthetic was ∆AP
50
= 14.0, and for the
JA → CA was ∆AP
50
= 9.4, which is a significant dif-
ference in the effectiveness of the models.
Using only synthetic data for model training can
significantly speed up the process of developing mod-
els (Majewski et al., 2022); however, based on the
results obtained in this research, we can observe that
this is associated with the risk of losing model accu-
racy. This observation is confirmed by the results af-
ter the 1st and 2nd stages of domain adaptation for
inference out-domain in Table 3 (the only_synthetic
approach was characterized by ∆AP
50
= 8.5 lower
AP
50
than the real_synthetic approach in the 1st stage
and by ∆AP
50
= 4.4 lower AP
50
in the 2nd second).
The lack of real data in the training set mostly af-
fects the results for in-domain inference in Table 5
(∆AP
50
= 11.3 difference between only_synthetic and
real_synthetic approaches).
When considering the results separately for each
of the defined classes, it should be noted that objects
of the live larvae class are the easiest to detect (av-
erage AP
50
after 2nd stage – 81.8) after a domain
change, while objects of the pupae class – the most
difficult (average AP
50
after 2nd stage – 66.6). This
is consistent with initial conclusions from data explo-
ration based on FID values in Table 2.
Quantitative indicators confirm the importance of
augmentation in 1st stage for the real_synthetic ap-
proach. Additional samples complement the relevant
places in the feature space and can expand the distri-
bution of features for a given class.
Analyzing the results from 2nd stage for the
two proposed strategies in Table 3 for "mixed
source/target samples" strategy and in Table 4 for
"only target samples" strategy, we can conclude that
the "mixed source/target samples" strategy is the most
suitable for creating a training set, which is confirmed
by obtaining an increased AP
50
from 65.5 to 71.8
compared to the "only target samples" strategy.
A summary of the most important results ob-
VISAPP 2023 - 18th International Conference on Computer Vision Theory and Applications
384
Table 3: Comparison of proposed domain adaptation methods for mixed source/target domain samples strategy.
case type method
AP
50
stage 1. stage 2. (mixing strategy)
live l. dead l. pup. avg. live l. dead l. pup. avg.
CA → LU
only_real 64.9 58.9 74.7 66.2 79.9 59.7 75.4 71.7
real_synthetic 70.7 61.0 78.0 69.9 82.8 61.7 78.1 74.2
only_synthetic 63.3 54.0 65.5 60.9 82.1 62.0 77.4 73.8
CA → JA
only_real 69.7 50.4 29.2 49.8 75.3 55.1 36.7 55.7
real_synthetic 72.2 38.5 27.6 46.1 76.0 55.0 36.5 55.8
only_synthetic 59.3 28.6 18.6 35.5 77.3 40.8 31.7 49.9
LU → CA
only_real 41.3 58.5 39.5 46.4 79.7 78.1 69.4 75.7
real_synthetic 65.1 68.8 47.2 60.4 80.2 76.4 69.6 75.4
only_synthetic 64.3 69.3 49.9 61.2 79.8 76.7 70.6 75.7
LU → JA
only_real 73.8 53.8 28.7 52.1 83.3 66.9 56.2 68.8
real_synthetic 74.1 45.9 26.2 48.7 84.9 62.3 62.9 70.0
only_synthetic 59.3 31.6 14.7 35.2 83.2 50.5 55.2 63.0
JA → CA
only_real 76.8 67.1 47.8 63.9 84.6 76.4 66.8 75.9
real_synthetic 78.8 73.3 67.7 73.3 82.9 76.3 69.5 76.2
only_synthetic 71.4 73.6 61.1 68.7 78.5 72.8 59.8 70.4
JA → LU
only_real 75.2 68.3 71.5 71.7 83.2 74.3 84.2 80.6
real_synthetic 82.9 71.5 82.7 79.0 84.2 70.6 82.8 79.2
only_synthetic 74.2 60.3 60.3 64.9 79.6 61.9 73.3 71.6
all (summary)
only_real 67.0 59.5 48.6 58.4 81.0 68.4 64.8 71.4
real_synthetic 74.0 59.8 54.9 62.9 81.8 67.1 66.6 71.8
only_synthetic 65.3 52.9 45.0 54.4 80.1 60.8 61.3 67.4
Table 4: Results for the only target samples strategy for the 2nd stage of domain adaptation.
case type method
AP
50
stage 2. (only target samples strategy)
live larvae dead larvae} pupae average
all (summary)
only_real 76.6 60.9 55.8 64.5
real_synthetic 78.5 59.1 59.0 65.5
only_synthetic 77.5 54.7 54.2 62.2
Table 5: Reference values for domain adaptation as AP
50
values for in-domain inference.
source type method
AP
50
live larvae dead larvae pupae average
all (summary)
only_real 86.6 78.8 84.4 83.3
real_synthetic 88.4 81.8 85.2 85.2
only_synthetic 80.0 71.6 70.3 73.9
tained in the study is presented on the radar plot
in Figure 4. In Figure 4 it can be seen that for the
crossings JA → CA, JA → LU, CA → LU, already
1st stage of the proposed method based on augmen-
tation achieves reasonable AP results when using the
real_synthetic approach. The 2nd stage caused a sig-
nificant increase in AP for the crossings LU → JA and
LU → CA. After the two stages of the proposed solu-
tion, the final value of the obtained AP values strongly
depended on the target domain. For crossings where
the target domain was JA, the final AP values were
the lowest (AP
50
=55.8, AP
50
=70). In summary, the
best variation of the proposed method made it pos-
sible to increase the average AP
50
from 58.4 to 62.9
after 1st stage and to 71.8 after the 2nd stage. To ob-
tain as high AP
50
values as in-domain trained mod-
els (AP
50
= 85.2), additional labelling should be per-
formed, especially of objects undetected by models
after the 2nd stage of adaptation. The obtained AP
50
level between 70 and 80.6 for 5 out of 6 ( except for
CA → JA) types of crossings between domains makes
it possible to improve additional labelling by label
proposals that are predictions of the model obtained
after the 2nd stage.
Mixing Augmentation and Knowledge-Based Techniques in Unsupervised Domain Adaptation for Segmentation of Edible Insect States
385
Figure 4: Comparison of proposed domain adaptation meth-
ods for different cases.
To confirm the good quality of predictions after
domain adaptation, Figure 5 compares the predictions
by the in-domain trained model with the predictions
of the model after domain adaptation for three se-
lected samples from different target domains. For
clarity, Figure 5 shows the predictions only for the
dead larvae and pupae classes.
Figure 5: Comparison of predictions with ground truth for
in-domain and out-domain inference cases.
4 CONCLUSIONS
The proposed two-stage method for domain adapta-
tion made it possible to significantly increase the effi-
ciency of object detection (AP
50
increased from 58.4
to 71.8) when changing the domain without additional
user supervision. The best results were obtained when
the final training set consisted of real samples from
the source domain, synthetic samples from the source
domain and synthetic samples from the target domain
(associated with filtered objects from the target do-
main). It confirms the validity of mixing real and syn-
thetic samples in the training set and mixing objects
from the source and target domains. It can also be
concluded from the results that using only synthetic
data when training models can significantly reduce
the efficiency of the models for both in-domain and
out-domain inference. The study showed the impor-
tance of augmentation techniques and consideration
of a priori knowledge for domain adaptation.
The proposed method is flexible and can be ex-
tended to other classes of objects representing states
of edible insects, e.g., beetles. The method’s exten-
sion would include adding new rules when filtering
the prediction based on a priori knowledge. The de-
veloped solutions will undoubtedly help rapidly adapt
monitoring systems for breeding the Tenebrio Molitor
to new breeding conditions.
Future research should focus on increasing the
quality of synthetic data. An interesting research di-
rection is to develop synthetic images based only on a
priori knowledge independently of a specific domain.
This approach could obtain a basic model not overfit-
ted on a particular domain.
ACKNOWLEDGEMENTS
We wish to thank Mariusz Mrzygłód for developing
applications for the designed data acquisition work-
station. We wish to thank Paweł Górzy
´
nski and
Dawid Biedrzycki from Tenebria (Lubawa, Poland)
for providing a data source of boxes with Tenebrio
Molitor. The work presented in this publication was
carried out within the project “Automatic mealworm
breeding system with the development of feeding
technology” under Sub-measure 1.1.1 of the Smart
Growth Operational Program 2014-2020 co-financed
from the European Regional Development Fund on
the basis of a co-financing agreement concluded with
the National Center for Research and Development
(NCBiR, Poland); grant POIR.01.01.01-00-0903/20.
VISAPP 2023 - 18th International Conference on Computer Vision Theory and Applications
386
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