I-AM-Bird: A Deep Learning Approach to Detect Amazonian Bird

Species in Residential Environments

Lucas Ferro Zampar

1 a

and Clay Palmeira da Silva

1,2 b

Federal University of Amap

a, Macap

a, Brazil

Center for Sustainable Computing, University of Huddersﬁeld, U.K.

Keywords:

Deep Learning, Object Detection, Faster R-CNN, Bird Species.

Abstract:

The Amazon presents several challenges, such as recognizing and monitoring its birdlife. It is known that bird

records are shared by many bird watchers in citizen science initiatives, including by residents who observe

birds feeding at their home feeders. In this context, the work proposed an approach based on deep learning

to automatically detect species of Amazonian birds that frequent residential feeders. To this end, a data set

consisting of 940 images captured by 3 webcams installed in a residential feeder was collected. In total, 1,836

birds of 5 species were recorded and annotated. Then, we used the dataset to train different conﬁgurations

of the Faster R-CNN detector. Considering the IoU threshold at 50%, the best model achieved an mAP of

98.33%, an mean precision of 95.96%, and an mean recall of 98.82%. The results also allow us to drive future

works to develop a monitoring system for these species in a citizen science initiative.

1 INTRODUCTION

The Amazon presents several cultural and scientiﬁc

challenges. One of them concerns the process of iden-

tifying and monitoring local birdlife. However, cur-

rent Artiﬁcial Intelligence (AI) advances offer new

approaches to gradually reduce these difﬁculties. One

such approach is the automatic detection of bird

species in images.

Bird records are shared in many citizen science

initiatives. For example, bird watchers can publicly

share images of birds on platforms like (WikiAves,

2023) and (eBird, 2023). In this way, they contribute

to ornithological research that studies the migration of

urban birds, climate change, distribution, and species

conservation (Barbosa et al., 2021).

Sharing records can also occur with the help of

feeders where food is placed to attract birds for ob-

servation (Alexandrino et al., 2022). For exam-

ple, the Feeder Watch project has asked its partici-

pants to identify and count the birds that have visited

their home feeders over 30 years, which has already

contributed to dozens of studies (Bonter and Greig,

2021).

Watching birds feeding in residential gardens also

https://orcid.org/0009-0009-5462-5114

https://orcid.org/0000-0003-0438-581X

positively impacts the well-being and connection with

the nature of local residents (White et al., 2023). Fur-

thermore, providing their food can stimulate the per-

ception of contribution to the lives of these animals

(Dayer et al., 2019).

Currently, Deep Learning (DL) is widely used

to automate image analysis in computer vision. DL

models, known as neural networks, are composed of

multiple layers and can automatically learn different

representations of the data with which they are trained

(LeCun et al., 2015). This makes DL models suitable

for tasks such as object detection in images.

In this task, the model must locate the objects

of interest in the image using rectangular bound-

ing boxes, in addition to identifying their respective

classes (Zaidi et al., 2022). For example, a detector

could locate birds and classify their species in an im-

age.

The architecture of a modern detection model con-

sists of the backbone network, responsible for ex-

tracting features from the images, and the head that

performs the detections (Bochkovskiy et al., 2020).

These models are generally based on convolutional

neural networks (CNNs).

For example, Faster R-CNN (Ren et al., 2015) is

a model that proposes Regions of Interest (RoI) from

which objects are detected. Other algorithms perform

detections without the proposal of regions, such as

542

Zampar, L. and Palmeira da Silva, C.

I-AM-Bird: A Deep Learning Approach to Detect Amazonian Bird Species in Residential Environments.

DOI: 10.5220/0012464500003636

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 16th International Conference on Agents and Artiﬁcial Intelligence (ICAART 2024) - Volume 2, pages 542-548

ISBN: 978-989-758-680-4; ISSN: 2184-433X

SSD (Liu et al., 2016) and YOLO (Redmon et al.,

2016).

In this context, Faster R-CNN is classiﬁed as a

two-stage detector, while SSD and YOLO are single-

stage. In general, two-stage detectors tend to be more

accurate at the cost of longer inference time (Elgendy,

2020).

In this scenario, our work proposes automatically

detecting Amazonian bird species that visit residen-

tial feeders with the Faster R-CNN model. For train-

ing purposes, we captured images of birds feeding

through webcams installed in the feeder of a resi-

dence in the town of Santana, in the Brazilian state

of Amap

a, located in the Amazonian region.

To the best of our knowledge, this is the ﬁrst work

to propose the detection of Amazonian bird species

from images collected at residential feeders. Addi-

tionally, we also created a dataset of images of these

species annotated for the object detection task.

2 RELATED WORK

In scientiﬁc literature, it is possible to ﬁnd works that

focus exclusively on the classiﬁcation of bird species.

For example, in the work of (Srijan et al., 2021), dif-

ferent CNN architectures were investigated to classify

260 species. (Huang and Basanta, 2021) also studied

CNNs in classifying 29 endemic species of Tawain.

In both cases, a mobile application was developed.

In the work of (Pinheiro and Soares, 2021), im-

ages of 64 species of birds from the Brazilian state of

Esp

ırito Santo were collected, in addition to the train-

ing of the ResNet101 model, which achieved an ac-

curacy of 70.12 % with validation data. The authors

highlighted the difﬁculty faced by the model in scenes

with multiple birds. Object detection becomes more

suitable for these cases when locating and classifying

several objects in the scene.

In this sense, works dedicated to detecting birds

within the context of preventing accidents or failures

were found. (Alqaysi et al., 2021) studied different

conﬁgurations of the YOLOv4 model to detect birds

ﬂying close to wind farms. (Shi et al., 2021) em-

ployed an adapted YOLOv5 model to detect birds ﬂy-

ing around airports.

In both cases, the bird species were not taken into

account. (Qiu et al., 2022) used the YOLOv4-tiny

model to detect 20 species of birds related to faults in

power transmission lines.

Other work has focused on detecting species in

natural scenes. (Mao et al., 2021) used the domain

randomization technique to train the Faster R-CNN

model to detect 2 species found in an ecological park.

(Xiang et al., 2022) already proposed improving the

Faster R-CNN model to detect 10 species on small

scales in another park.

The closest work found collected images from

transmissions carried out by the Cornell Lab of Or-

nithology that show birds feeding (Mirugwe et al.,

2022). From there, they used the images to train dif-

ferent Faster R-CNN and SSD model conﬁgurations.

However, the study did not consider the species of

birds, nor did it collect original images.

3 METHODOLOGY

This section will detail the work development stages,

including data acquisition, training, and evaluation of

the models. All codes developed and the dataset col-

lected are publicly available on the authors’ GitHub

by the following link https://github.com/Lucas-Zam

par/amazonian birds detector.git.

We highlight that due to the lack of previous

studies, creating and deﬁning a baseline was neces-

sary. Therefore, we called the baseline the model that

serves as a basis for analysis to be compared and im-

proved later, which will be disclosed in the next sec-

tion.

3.1 Data Aquisition

Food such as seeds and fruits were placed to at-

tract birds to the residence’s feeder. The birds were

recorded feeding by 3 Logitech C270 webcams in-

stalled on the feeder columns. The cameras were

connected to a notebook for capturing and storing

the recordings. The ﬁgures 1 and 2 show the feeder

used in the residence and the general bird recording

scheme, respectively.

Figure 1: Residential feeder.

I-AM-Bird: A Deep Learning Approach to Detect Amazonian Bird Species in Residential Environments

543

Figure 2: General recording scheme.

The recordings were carried out between

07/09/2022 and 14/09/2022, mostly from 11am to

5pm. The recordings from days 7 to 13 were man-

ually trimmed to exclude moments in which birds

were absent. The resulting clippings were organized

based on the date of recording and the predominant

species. It is worth noting that the recordings from

the day 14 did not participate in this process. Instead,

they are being reserved for inference.

From the clippings, 940 frames that make up the

images in the dataset were extracted. The images

were uploaded to the RoboFlow platform to be an-

notated. The annotation process consisted of drawing

a bounding box over each bird to locate it and associ-

ating it with its species for classiﬁcation.

As a result, 1,836 annotations were made in Pas-

cal VOC format. In addition, the work identiﬁed 5

species popularly known as orange-fronted yellow-

ﬁnch (Sicalis columbiana), shiny cowbird (Molothrus

bonariensis), ground dove (Columbina spp.), blue-

gray tanager (Thraupis episcopus) and palm tanager

(Thraupis palmarum).

Two distinct ground dove species were identiﬁed,

but it was decided to group them in the same class.

The species were identiﬁed by consulting the citi-

zen science platforms (WikiAves, 2023) and (eBird,

2023), in addition to using a regional bird manual

(d’Affonseca et al., 2012).

3.2 Training

Model training used IceVision, a high-level Python

framework for computer vision tasks. The models

used are pre-trained and made available by IceVision

through the MMDetection project. The training was

divided into two consecutive phases, preliminary and

ﬁnal.

3.2.1 Preliminary Phase

In the preliminary phase, 30% of the data was pre-

served in a set called partial, corresponding to 282

images and 560 annotations. This decision was made

to deﬁne a baseline with less training data.

The need to deﬁne a baseline arises due to the lack

of previous studies. Therefore, it is necessary to de-

ﬁne a model that serves as a basis for analysis to be

compared and improved later, which will be done in

the next phase.

During the preliminary phase, 70% of the partial

set was used for training, while the remaining 30%

was used for validation. The purpose is to establish

our baseline training conﬁguration. Therefore, the

preliminary phase is still subdivided into two consec-

utive steps: backbone selection and hyperparameter

tuning.

The objective of the ﬁrst step was to select the

model backbone with the best performance. To this

end, 8 backbones of the ResNet (He et al., 2016)

and ResNeXt (Xie et al., 2017) types were tested.

All backbones were followed by the Feature Pyramid

Network (FPN) (Lin et al., 2017), which helps detect

objects at multiple scales.

Regarding ResNet backbones, networks with 50

and 101 layers were experimented with. Regarding

the ResNeXt backbones, the networks had 101 layers

but with 32 and 64 convolution blocks. Each back-

bone was pre-trained by MMDetection with a learn-

ing rate schedule of 1x and 2x. Table 1 summarizes

the tried backbones.

Table 1: Backbone conﬁguration.

Backbone Layers Conv. Groups LR Schedule

ResNet 50 - 1x

ResNet 50 - 2x

ResNet 101 - 1x

ResNet 101 - 2x

ResNeXt 101 32x4d 1x

ResNeXt 101 32x4d 2x

ResNeXt 101 64x4d 1x

ResNeXt 101 64x4d 2x

The objective of the second step was to ﬁnd some

hyperparameter adjustments that would improve the

performance of the previous model. In this way, 13

new models were trained with the selected backbone

but with variations in hyperparameters such as the to-

tal number of epochs, batch size, learning rate, image

size, and presizing.

It is worth highlighting that presizing is a tech-

nique that applies transformations sequentially to the

image, requiring a single interpolation at the end

(Howard and Gugger, 2020). Therefore, the image

must initially be resized to a larger size than the train-

ing size.

At the end of the preliminary phase, the model

trained with the backbone selected in the ﬁrst stage

and with the best hyperparameter adjustment found

in the second stage was deﬁned as the baseline.

ICAART 2024 - 16th International Conference on Agents and Artiﬁcial Intelligence

544

3.2.2 Final Phase

In the ﬁnal phase, a single deﬁnitive model was

trained using the same training conﬁguration as the

baseline. However, all data corresponding to 940 im-

ages and 1,836 annotations was used at this phase.

70% of the data was intended for training, while the

remaining 30% for validation. After training, the per-

formance of the deﬁnitive model was compared with

that of the baseline.

3.3 Evaluation

The primary evaluation metric in object detection

is the mean Average Precision (mAP), which corre-

sponds to the arithmetic mean of the areas under the

precision-recall curve for each class (Padilla et al.,

2021). To calculate the mAP, deﬁning a minimum

level of overlap between the actual and predicted

bounding boxes, represented by the Intersection over

Union (IoU) metric is necessary (Elgendy, 2020).

We adopted a minimum IoU of 50% and mAP as

the primary evaluation metric. Precision and recall

metrics were calculated for each species as a refer-

ence. Additionally, the FiftyOne framework, which

has several tools for evaluating the models, was used

to calculate all the metrics.

4 RESULTS

This section will present the most signiﬁcant results

achieved in our work in each training phase. It is

worth noting that all training was conducted on the

RTX 2060 S GPU with 8 GB of VRAM.

4.1 Preliminary Phase

During the backbone selection step, the original train-

ing conﬁguration was deﬁned with 20 epochs, batch

size equal to 1, learning rate at 10

−4

, and image size at

512x512 without using the presizing technique. In Ta-

ble 2, it is possible to check the mAP value achieved

by each backbone with the validation data.

The backbone that led to the best performance was

the ResNeXt 101 FPN 32x4d 1x, reaching mAP equal

to 94.17%. One point to highlight is that all ResNeXt-

type backbones achieved better metrics than ResNet-

type backbones.

During the hyperparameter tuning stage, the per-

formance improvement was noted by increasing the

size of the training image to 812x812 and employ-

ing the presizing technique with initial resizing to

Table 2: Performance in the backbone selection stage.

Backbone mAP

ResNet 50 FPN 1x 87.11 %

ResNet 50 FPN 2x 87.18 %

ResNet 101 FPN 1x 87.23 %

ResNet 101 FPN x 87.67 %

ResNeXt 101 FPN 32x4d 1x 94.17 %

ResNeXt 101 FPN 32x4d 2x 88.70 %

ResNeXt 101 FPN 64x4d 1x 92.80 %

ResNeXt 101 FPN 64x4d 2x 88.74 %

1024x1024. The model trained under these condi-

tions achieved a mAP of 94.59%, then deﬁned as the

baseline. The table 3 presents the precision and recall

values achieved by the baseline for each species.

Table 3: Precision and recall for each baseline species.

Specie Precision Recall

orange-fronted

yellow-ﬁnch

93.44% 98.28%

2 shiny cowbird 90.00% 92.31%

3 ground dove 96.43% 100.00%

4 blue-gray tanager 85.19% 100.00%

5 palm tanager 84.62% 88.00%

Mean 89.93 % 95.72 %

It is noted that both the lowest precision and the

lowest recall occurred for the palm tanager species.

The most excellent precision was for the ground dove

species, while the greatest recall was for the ground

dove and the blue-gray tanager species. On average,

precision and recall were 89.93% and 95.72%, re-

spectively.

4.2 Final Phase

The deﬁnitive model was trained for 20 epochs, with

batch size equal to 1, learning rate at 10

−4

, and im-

age size at 812x812 with initial resizing to the size of

1024x1024 during presizing. The performance of the

deﬁnitive model is compared with that of the baseline

in the 4 table.

Table 4: Performance between the baseline and the deﬁni-

tive model.

Model mAP

Baseline 94.59%

Deﬁnitive model 98.33%

There was a percentage growth of 3.95% in the

mAP ratio, demonstrating that training with more data

beneﬁted the deﬁnitive model. In table 5, it is possi-

I-AM-Bird: A Deep Learning Approach to Detect Amazonian Bird Species in Residential Environments

545

ble to check the precision and recall metrics of the

deﬁnitive model for each species.

Table 5: Precision and recall for each species of the deﬁni-

tive model.

Specie Precision Recall

orange-fronted

yellow-ﬁnch

93.44% 98.28%

2 shiny cowbird 97.25% 100.00%

3 ground dove 96.63% 97.73%

4 blue-gray tanager 98.77% 100.00%

5 palm tanager 92.00% 97.87%

Mean 95.96% 98.82%

The deﬁnitive model achieved higher precision

and recall values than the baseline for each species,

except for the reduction in recall from 100% to

97.73% concerning the ground dove species. Further-

more, it is notable that the lowest precision still occurs

for the palm tanager species, despite its value having

increased from 84.62% to 92%, reﬂecting a percent-

age growth of 8.72%.

In general, the deﬁnitive model presented an av-

erage percentage gain in precision of 6.7% compared

to the baseline, going from 89.93% to 95.96%. The

mean recall had a more negligible percentage growth

of 3.24%, going from 95.72% to 98.82%.

In the ﬁgures 3, 4, 5 and 6 it is possible to check

detections carried out for different species. The im-

ages were extracted from the recordings on day 14

and do not belong to the training or validation data.

The number next to the species name is the conﬁ-

dence level, a probability that reﬂects how conﬁdent

the model is of detection.

It is essential to highlight that the baseline we

deﬁned was necessary due to the lack of any previ-

ous work related to detecting Amazonian bird species

that visit home feeders. The closest work carried out

by (Mirugwe et al., 2022) achieved the best mAP of

98.5% but did not focus on detecting bird species, nor

did it use original images.

Instead, it focused only on detecting birds feeding

in images collected from Cornell Lab video streams.

Since it is the only work focusing on detecting birds

in feeders, it was selected for comparison. It is worth

noting that we use less data and multiple classes re-

lated to each species.

5 CONCLUSION

In this work, we investigated detecting Amazonian

bird species that visit residential feeders using the

Faster R-CNN model. As a result, the work pro-

duced a set of 940 images and 1,836 annotations of 5

Figure 3: Detection of the blue-gray tanager species.

Figure 4: Detection of the palm tanager species.

Figure 5: Detection of the orange-fronted yellow-ﬁnch,

ground dove and palm tanager species.

Figure 6: Detection of orange-fronted yellow-ﬁnch and

shiny cowbird species.

species known as orange-fronted yellow-ﬁnch, shiny

cowbird, ground dove, blue-gray tanager, and palm

tanager. With this data, different conﬁgurations of the

ICAART 2024 - 16th International Conference on Agents and Artiﬁcial Intelligence

546

Faster R-CNN model were trained in two phases. In

the preliminary phase, a baseline was deﬁned, achiev-

ing an mAP of 94.59%, mean precision of 89.93%,

and mean recall of 95.72%. In the ﬁnal phase, a

deﬁnitive model trained on more data achieved an

mAP of 98.33%, an mean precision of 95.96%, and

an mean recall of 98.82%. Given these results, the

work demonstrated the feasibility of applying a deep-

learning approach to detect the species that visit the

feeder of the residence in question. Therefore, there

is an opportunity to develop future work that seeks to

implement a monitoring system for these species in a

citizen science initiative to study them and contribute

to their preservation.

6 FUTURE WORKS

Given the results achieved, it is possible to visualize

future work. New images will be collected and anno-

tated in the short term at the same feeder to increase

the data set. In this case, the annotation can be par-

tially automated using the deﬁnitive model.

Images featuring the highlighted birds will be col-

lected on citizen science platforms in the medium

term. Pre-training models with these images can help

them learn richer characteristics of the species, which

can contribute to increased performance and general-

ization capacity.

A system with the Raspberry PI board capable of

acquiring new images autonomously in other homes

using cloud computing will be developed in the long

term. Furthermore, the feasibility of performing de-

tections locally in an AI-on-the-edge approach will be

studied.

REFERENCES

Alexandrino, E. R., Camboim, T. A., Chaves, F. G., Bovo,

A. A. A., da Silva, M. A. G., da Silva, D. A. M.,

Moss, M., Souza, T. P., de Oliveira Santos, C., de Mat-

tos Brito, C. B., et al. (2022). Which birds are brazil-

ians seeing on urban and non-urban feeders? an anal-

ysis based on a collective online birding. Ornithology

Research, 30(2):104–117.

Alqaysi, H., Fedorov, I., Qureshi, F. Z., and O’Nils, M.

(2021). A temporal boosted yolo-based model for

birds detection around wind farms. Journal of imag-

ing, 7(11):227.

Barbosa, K., Develey, P., Ribeiro, M., and Jahn, A. (2021).

The contribution of citizen science to research on mi-

gratory and urban birds in brazil. ornithol res 29: 1–

11.

Bochkovskiy, A., Wang, C.-Y., and Liao, H.-Y. M. (2020).

Yolov4: Optimal speed and accuracy of object detec-

tion. arXiv preprint arXiv:2004.10934.

Bonter, D. N. and Greig, E. I. (2021). Over 30 years of stan-

dardized bird counts at supplementary feeding stations

in north america: A citizen science data report for

project feederwatch. Frontiers in Ecology and Evo-

lution, 9:619682.

Dayer, A. A., Rosenblatt, C., Bonter, D. N., Faulkner, H.,

Hall, R. J., Hochachka, W. M., Phillips, T. B., and

Hawley, D. M. (2019). Observations at backyard bird

feeders inﬂuence the emotions and actions of people

that feed birds. People and Nature, 1(2):138–151.

d’Affonseca, A., Cohn-Haft, M., and Macedo, I. T. d.

(2012). Aves da regi

ao de Manaus. Editora INPA.

eBird (2023). ebird - discover a new world of birding...

https://ebird.org/home. Accessed: 2023-11-20.

Elgendy, M. (2020). Deep learning for vision systems. Man-

ning.

He, K., Zhang, X., Ren, S., and Sun, J. (2016). Deep resid-

ual learning for image recognition. In Proceedings of

the IEEE conference on computer vision and pattern

recognition, pages 770–778.

Howard, J. and Gugger, S. (2020). Deep Learning for

Coders with fastai and PyTorch. O’Reilly Media.

Huang, Y.-P. and Basanta, H. (2021). Recognition of en-

demic bird species using deep learning models. Ieee

Access, 9:102975–102984.

LeCun, Y., Bengio, Y., and Hinton, G. (2015). Deep learn-

ing. nature, 521(7553):436–444.

Lin, T.-Y., Doll

ar, P., Girshick, R., He, K., Hariharan, B.,

and Belongie, S. (2017). Feature pyramid networks

for object detection. In Proceedings of the IEEE con-

ference on computer vision and pattern recognition,

pages 2117–2125.

Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S.,

Fu, C.-Y., and Berg, A. C. (2016). Ssd: Single shot

multibox detector. In Computer Vision–ECCV 2016:

14th European Conference, Amsterdam, The Nether-

lands, October 11–14, 2016, Proceedings, Part I 14,

pages 21–37. Springer.

Mao, X., Chow, J. K., Tan, P. S., Liu, K.-f., Wu, J., Su, Z.,

Cheong, Y. H., Ooi, G. L., Pang, C. C., and Wang,

Y.-H. (2021). Domain randomization-enhanced deep

learning models for bird detection. Scientiﬁc reports,

11(1):639.

Mirugwe, A., Nyirenda, J., and Dufourq, E. (2022). Au-

tomating bird detection based on webcam captured

images using deep learning. In Proceedings of 43rd

Conference of the South African Insti, volume 85,

pages 62–76.

Padilla, R., Passos, W. L., Dias, T. L., Netto, S. L., and

Da Silva, E. A. (2021). A comparative analysis of ob-

ject detection metrics with a companion open-source

toolkit. Electronics, 10(3):279.

Pinheiro, B. and Soares, L. (2021). Sistema de reconheci-

mento autom

atico de p

assaros da fauna de linhares-es

utilizando redes neurais convolucionais. In Simp

osio

Brasileiro de Automac¸

ao Inteligente-SBAI, volume 1.

Qiu, Z., Zhu, X., Liao, C., Shi, D., Kuang, Y., Li, Y., and

Zhang, Y. (2022). Detection of bird species related to

I-AM-Bird: A Deep Learning Approach to Detect Amazonian Bird Species in Residential Environments

547

transmission line faults based on lightweight convolu-

tional neural network. IET Generation, Transmission

& Distribution, 16(5):869–881.

Redmon, J., Divvala, S., Girshick, R., and Farhadi, A.

(2016). You only look once: Uniﬁed, real-time object

detection. In Proceedings of the IEEE conference on

computer vision and pattern recognition, pages 779–

788.

Ren, S., He, K., Girshick, R., and Sun, J. (2015). Faster

r-cnn: Towards real-time object detection with region

proposal networks. Advances in neural information

processing systems, 28.

Shi, X., Hu, J., Lei, X., and Xu, S. (2021). Detection of

ﬂying birds in airport monitoring based on improved

yolov5. In 2021 6th International Conference on In-

telligent Computing and Signal Processing (ICSP),

pages 1446–1451. IEEE.

Srijan, Samriddhi, and Gupta (2021). Mobile application

for bird species identiﬁcation using transfer learn-

ing. In 2021 IEEE International Conference on Ar-

tiﬁcial Intelligence in Engineering and Technology

(IICAIET), pages 1–6. IEEE.

White, M. E., Hamlin, I., Butler, C. W., and Richardson, M.

(2023). The joy of birds: The effect of rating for joy

or counting garden bird species on wellbeing, anxiety,

and nature connection. Urban Ecosystems, pages 1–

11.

WikiAves (2023). Wiki aves - a enciclop

edia das aves do

brasil. https://www.wikiaves.com.br/. Accessed:

2023-11-20.

Xiang, W., Song, Z., Zhang, G., and Wu, X. (2022). Birds

detection in natural scenes based on improved faster

rcnn. Applied Sciences, 12(12):6094.

Xie, S., Girshick, R., Doll

ar, P., Tu, Z., and He, K. (2017).

Aggregated residual transformations for deep neural

networks. In Proceedings of the IEEE conference on

computer vision and pattern recognition, pages 1492–

1500.

Zaidi, S. S. A., Ansari, M. S., Aslam, A., Kanwal, N., As-

ghar, M., and Lee, B. (2022). A survey of modern

deep learning based object detection models. Digital

Signal Processing, 126:103514.

ICAART 2024 - 16th International Conference on Agents and Artiﬁcial Intelligence

548