Browse > Article

Implementation of a Classification System for Dog Behaviors using YOLI-based Object Detection and a Node.js Server  

Jo, Yong-Hwa (Department of Information & Communication Engineering, Kyungnam University)
Lee, Hyuek-Jae (Department of Information & Communication Engineering, Kyungnam University)
Kim, Young-Hun (Department of Naval Architecture & Ocean System Engineering, Kyungnam University)
Publication Information
Journal of the Institute of Convergence Signal Processing / v.21, no.1, 2020 , pp. 29-37 More about this Journal
Abstract
This paper implements a method of extracting an object about a dog through real-time image analysis and classifying dog behaviors from the extracted images. The Darknet YOLO was used to detect dog objects, and the Teachable Machine provided by Google was used to classify behavior patterns from the extracted images. The trained Teachable Machine is saved in Google Drive and can be used by ml5.js implemented on a node.js server. By implementing an interactive web server using a socket.io module on the node.js server, the classified results are transmitted to the user's smart phone or PC in real time so that it can be checked anytime, anywhere.
Keywords
Convolutional Neural Network (CNN); Darknet YOLO; Object detection; Deep learning; Dog behaviors;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Fitbark, "Fitbark," Available: https://www.fitbark.com.
2 J. Redmon, A. Farhadi, "Yolo9000 better, faster, stronger," in the IEEE Conference on Computer Vision and Pattern Recognition, 2017.
3 Available: https://github.com/AlexeyAB/darknet
4 Available: https://medium.com/@manivannan_data/how-to-train-yolov3-to-detect-custom-objects-ccbcafeb13d2
5 Available: https://bit.ly/3gJVWPC, NEWSRO 2019.
6 Available: https://bit.ly/3gLuq4t, 2020.
7 R. Brugarolas, R. T. Loftin, P. Yang, D. L. Roberts, B. Sherman, A. Bozkurt, "Behavior recognition based on machine learning algorithms for a wireless canine machine interface," in 2013 IEEE International Conference on Body Sensor Networks, Cambridge, MA, USA, 2013.
8 J. Redmon, A. Farhadi, "Yolo v3: An incremental improvement," Available: https://arxiv.org/abs/1804.02767, 2018.
9 Available: https://teachablemachine.withgoogle.com.
10 Available: http://topdogblog1.blogspot.com/2012/04/doggie-language.html
11 Available: https://www.kaggle.com/tongpython/cat-and-dog
12 Available: https://github.com/AlexeyAB/Yolo_mark
13 Available: https://nodejs.org/ko/download/