Detection and recognition of badgers using deep learning

Emmanuel Okafor; Gerard Berendsen; Lambert Schomaker; Marco Wiering

doi:10.1007/978-3-030-01424-7_54

Detection and recognition of badgers using deep learning

Emmanuel Okafor^*
, Gerard Berendsen
, Lambert Schomaker
, Marco Wiering

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Scopus citations

Abstract

This paper describes the use of two different deep-learning algorithms for object detection to recognize different badgers. We use recordings of four different badgers under varying background illuminations. In total four different object detection algorithms based on deep neural networks are compared: The single shot multi-box detector (SSD) with the Inception-V2 or MobileNet as a backbone, and the faster region-based convolutional neural network (Faster R-CNN) combined with Inception-V2 or residual networks. Furthermore, two different activation functions are compared to compute probabilities that some badger is in the detected region: the softmax and sigmoid functions. The results of all eight models show that SSD obtains higher recognition accuracies (97.8%–98.6%) than Faster R-CNN (84.8%–91.7%). However, the training time of Faster R-CNN is much shorter than that of SSD. The use of different output activation functions seems not to matter much.

Original language	English
Title of host publication	Artificial Neural Networks and Machine Learning – ICANN 2018 - 27th International Conference on Artificial Neural Networks, 2018, Proceedings
Editors	Yannis Manolopoulos, Barbara Hammer, Vera Kurkova, Lazaros Iliadis, Ilias Maglogiannis
Publisher	Springer Verlag
Pages	554-563
Number of pages	10
ISBN (Print)	9783030014230
DOIs	https://doi.org/10.1007/978-3-030-01424-7_54
State	Published - 2018
Externally published	Yes
Event	27th International Conference on Artificial Neural Networks, ICANN 2018 - Rhodes, Greece Duration: 4 Oct 2018 → 7 Oct 2018

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11141 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	27th International Conference on Artificial Neural Networks, ICANN 2018
Country/Territory	Greece
City	Rhodes
Period	4/10/18 → 7/10/18

Bibliographical note

Publisher Copyright:
© Springer Nature Switzerland AG 2018.

Keywords

Badger classification
Deep learning
Image recognition
Object detection

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-030-01424-7_54

Cite this

Okafor, E., Berendsen, G., Schomaker, L., & Wiering, M. (2018). Detection and recognition of badgers using deep learning. In Y. Manolopoulos, B. Hammer, V. Kurkova, L. Iliadis, & I. Maglogiannis (Eds.), Artificial Neural Networks and Machine Learning – ICANN 2018 - 27th International Conference on Artificial Neural Networks, 2018, Proceedings (pp. 554-563). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11141 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-01424-7_54

Okafor, Emmanuel ; Berendsen, Gerard ; Schomaker, Lambert et al. / Detection and recognition of badgers using deep learning. Artificial Neural Networks and Machine Learning – ICANN 2018 - 27th International Conference on Artificial Neural Networks, 2018, Proceedings. editor / Yannis Manolopoulos ; Barbara Hammer ; Vera Kurkova ; Lazaros Iliadis ; Ilias Maglogiannis. Springer Verlag, 2018. pp. 554-563 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{442374ad8349490fbffdc19d5dc0d26d,

title = "Detection and recognition of badgers using deep learning",

abstract = "This paper describes the use of two different deep-learning algorithms for object detection to recognize different badgers. We use recordings of four different badgers under varying background illuminations. In total four different object detection algorithms based on deep neural networks are compared: The single shot multi-box detector (SSD) with the Inception-V2 or MobileNet as a backbone, and the faster region-based convolutional neural network (Faster R-CNN) combined with Inception-V2 or residual networks. Furthermore, two different activation functions are compared to compute probabilities that some badger is in the detected region: the softmax and sigmoid functions. The results of all eight models show that SSD obtains higher recognition accuracies (97.8\%–98.6\%) than Faster R-CNN (84.8\%–91.7\%). However, the training time of Faster R-CNN is much shorter than that of SSD. The use of different output activation functions seems not to matter much.",

keywords = "Badger classification, Deep learning, Image recognition, Object detection",

author = "Emmanuel Okafor and Gerard Berendsen and Lambert Schomaker and Marco Wiering",

note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2018.; 27th International Conference on Artificial Neural Networks, ICANN 2018 ; Conference date: 04-10-2018 Through 07-10-2018",

year = "2018",

doi = "10.1007/978-3-030-01424-7\_54",

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Okafor, E, Berendsen, G, Schomaker, L & Wiering, M 2018, Detection and recognition of badgers using deep learning. in Y Manolopoulos, B Hammer, V Kurkova, L Iliadis & I Maglogiannis (eds), Artificial Neural Networks and Machine Learning – ICANN 2018 - 27th International Conference on Artificial Neural Networks, 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11141 LNCS, Springer Verlag, pp. 554-563, 27th International Conference on Artificial Neural Networks, ICANN 2018, Rhodes, Greece, 4/10/18. https://doi.org/10.1007/978-3-030-01424-7_54

Detection and recognition of badgers using deep learning. / Okafor, Emmanuel; Berendsen, Gerard; Schomaker, Lambert et al.
Artificial Neural Networks and Machine Learning – ICANN 2018 - 27th International Conference on Artificial Neural Networks, 2018, Proceedings. ed. / Yannis Manolopoulos; Barbara Hammer; Vera Kurkova; Lazaros Iliadis; Ilias Maglogiannis. Springer Verlag, 2018. p. 554-563 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11141 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Detection and recognition of badgers using deep learning

AU - Okafor, Emmanuel

AU - Berendsen, Gerard

AU - Schomaker, Lambert

AU - Wiering, Marco

N1 - Publisher Copyright: © Springer Nature Switzerland AG 2018.

PY - 2018

Y1 - 2018

N2 - This paper describes the use of two different deep-learning algorithms for object detection to recognize different badgers. We use recordings of four different badgers under varying background illuminations. In total four different object detection algorithms based on deep neural networks are compared: The single shot multi-box detector (SSD) with the Inception-V2 or MobileNet as a backbone, and the faster region-based convolutional neural network (Faster R-CNN) combined with Inception-V2 or residual networks. Furthermore, two different activation functions are compared to compute probabilities that some badger is in the detected region: the softmax and sigmoid functions. The results of all eight models show that SSD obtains higher recognition accuracies (97.8%–98.6%) than Faster R-CNN (84.8%–91.7%). However, the training time of Faster R-CNN is much shorter than that of SSD. The use of different output activation functions seems not to matter much.

AB - This paper describes the use of two different deep-learning algorithms for object detection to recognize different badgers. We use recordings of four different badgers under varying background illuminations. In total four different object detection algorithms based on deep neural networks are compared: The single shot multi-box detector (SSD) with the Inception-V2 or MobileNet as a backbone, and the faster region-based convolutional neural network (Faster R-CNN) combined with Inception-V2 or residual networks. Furthermore, two different activation functions are compared to compute probabilities that some badger is in the detected region: the softmax and sigmoid functions. The results of all eight models show that SSD obtains higher recognition accuracies (97.8%–98.6%) than Faster R-CNN (84.8%–91.7%). However, the training time of Faster R-CNN is much shorter than that of SSD. The use of different output activation functions seems not to matter much.

KW - Badger classification

KW - Deep learning

KW - Image recognition

KW - Object detection

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AN - SCOPUS:85054814019

SN - 9783030014230

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 554

EP - 563

BT - Artificial Neural Networks and Machine Learning – ICANN 2018 - 27th International Conference on Artificial Neural Networks, 2018, Proceedings

A2 - Manolopoulos, Yannis

A2 - Hammer, Barbara

A2 - Kurkova, Vera

A2 - Iliadis, Lazaros

A2 - Maglogiannis, Ilias

PB - Springer Verlag

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Y2 - 4 October 2018 through 7 October 2018

ER -

Okafor E, Berendsen G, Schomaker L, Wiering M. Detection and recognition of badgers using deep learning. In Manolopoulos Y, Hammer B, Kurkova V, Iliadis L, Maglogiannis I, editors, Artificial Neural Networks and Machine Learning – ICANN 2018 - 27th International Conference on Artificial Neural Networks, 2018, Proceedings. Springer Verlag. 2018. p. 554-563. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-01424-7_54

Detection and recognition of badgers using deep learning

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this