The distinguish between cats and dogs based on Detectron2 for automatic feeding

Research Article
Open access

The distinguish between cats and dogs based on Detectron2 for automatic feeding

Zijing Shi 1*
  • 1 United World College Changshu China    
  • *corresponding author zjshi21@uwcchina.org
Published on 7 February 2024 | https://doi.org/10.54254/2755-2721/38/20230526
ACE Vol.38
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-301-2
ISBN (Online): 978-1-83558-302-9

Abstract

With the rapid growth of urbanization, the problem of stray animals on the streets is particularly prominent, especially the shortage of food for cats and dogs. This study introduces an automatic feeding system based on the Detectron2 deep learning framework, aiming to accurately identify and provide suitable food for these stray animals. Through training using Detectron2 with a large amount of image data, the system shows extremely high recognition accuracy in single-object images. When dealing with multi-object images, Detectron2 can generate independent recognition frames for each target and make corresponding feeding decisions. Despite the outstanding performance of the model, its potential uncertainties and errors still need to be considered. This research not only offers a practical solution to meet the basic needs of stray animals but also provides a new perspective for urban management and animal welfare. By combining technology with social responsibility, this innovative solution opens up a new path for solving the stray animal problem in cities, with broad application prospects and profound social significance.

Keywords:

Detectron2, Automatic Feeding System, Deep Learning, Urban Management, Responsibility

Shi,Z. (2024). The distinguish between cats and dogs based on Detectron2 for automatic feeding. Applied and Computational Engineering,38,35-40.
Export citation

References

[1]. Abhishek A V S Kotni S 2021 Detectron2 object detection & manipulating images using cartoonization Int. J. Eng. Res. Technol. (IJERT) 10

[2]. He K Gkioxari G Dollár P Girshick R 2017 Mask R-CNN. In Proceedings of the IEEE international conference on computer vision pp 2961-2969

[3]. Krizhevsky A Sutskever I Hinton G E 2012 ImageNet classification with deep convolutional neural networks In Advances in neural information processing systems pp 1097-1105

[4]. Redmon J Divvala S Girshick R Farhadi A 2016 You Only Look Once: Unified, Real-Time Object Detection In Proceedings of the IEEE conference on computer vision and pattern recognition pp 779-788

[5]. Simonyan K Zisserman A 2014 Very deep convolutional networks for large-scale image recognition arXiv preprint arXiv:1409.1556.

[6]. Szegedy C Liu W Jia Y Sermanet P 2015 Going deeper with convolutions In Proceedings of the IEEE conference on computer vision and pattern recognition pp 1-9

[7]. Parkhi O M Vedaldi A Zisserman A et al. 2012 Cats and dogs 2012 IEEE conference on computer vision and pattern recognition IEEE pp 3498-3505

[8]. Zhang X Zhou X Lin M Sun J 2018 Shufflenet: An extremely efficient convolutional neural network for mobile devices In Proceedings of the IEEE conference on computer vision and pattern recognition pp 6848-6856

[9]. LeCun Y Bengio Y Hinton G 2015 Deep learning nature 521(7553): pp 436-444

[10]. Lin T Y Dollar P Girshick R 2017 Feature pyramid networks for object detection In Proceedings of the IEEE conference on computer vision and pattern recognition pp 2117-2125

[11]. Dataset https://www.kaggle.com/code/jaeboklee/pytorch-cat-vs-dog

Cite this article

Shi,Z. (2024). The distinguish between cats and dogs based on Detectron2 for automatic feeding. Applied and Computational Engineering,38,35-40.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

Disclaimer/Publisher's Note

The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

About volume

Volume title: Proceedings of the 2023 International Conference on Machine Learning and Automation

ISBN：978-1-83558-301-2(Print) / 978-1-83558-302-9(Online)
Editor：Mustafa İSTANBULLU
Conference website: https://2023.confmla.org/
Conference date: 18 October 2023
Series: Applied and Computational Engineering
Volume number: Vol.38
ISSN：2755-2721(Print) / 2755-273X(Online)

© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. Authors who publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See Open access policy for details).

References

[1]. Abhishek A V S Kotni S 2021 Detectron2 object detection & manipulating images using cartoonization Int. J. Eng. Res. Technol. (IJERT) 10

[2]. He K Gkioxari G Dollár P Girshick R 2017 Mask R-CNN. In Proceedings of the IEEE international conference on computer vision pp 2961-2969

[3]. Krizhevsky A Sutskever I Hinton G E 2012 ImageNet classification with deep convolutional neural networks In Advances in neural information processing systems pp 1097-1105

[4]. Redmon J Divvala S Girshick R Farhadi A 2016 You Only Look Once: Unified, Real-Time Object Detection In Proceedings of the IEEE conference on computer vision and pattern recognition pp 779-788

[5]. Simonyan K Zisserman A 2014 Very deep convolutional networks for large-scale image recognition arXiv preprint arXiv:1409.1556.

[6]. Szegedy C Liu W Jia Y Sermanet P 2015 Going deeper with convolutions In Proceedings of the IEEE conference on computer vision and pattern recognition pp 1-9

[7]. Parkhi O M Vedaldi A Zisserman A et al. 2012 Cats and dogs 2012 IEEE conference on computer vision and pattern recognition IEEE pp 3498-3505

[8]. Zhang X Zhou X Lin M Sun J 2018 Shufflenet: An extremely efficient convolutional neural network for mobile devices In Proceedings of the IEEE conference on computer vision and pattern recognition pp 6848-6856

[9]. LeCun Y Bengio Y Hinton G 2015 Deep learning nature 521(7553): pp 436-444

[10]. Lin T Y Dollar P Girshick R 2017 Feature pyramid networks for object detection In Proceedings of the IEEE conference on computer vision and pattern recognition pp 2117-2125

[11]. Dataset https://www.kaggle.com/code/jaeboklee/pytorch-cat-vs-dog

For authors
Guide for authors Policies for authors Aims and scope Subscription Open access policy
For editors
Policies for editors Editorial board
For reviewers
Policies for reviewers Privacy policy

Cookies are used by this site.
Copyright © 2024 EWA Publishing, its licensors, and contributors. All rights reserved, including text and data mining, AI training, and similar technologies.