Browse > Article
http://dx.doi.org/10.9723/jksiis.2021.26.5.001

Application of object detection algorithm for psychological analysis of children's drawing  

Yim, Jiyeon (한국원자력연구원 미래전략본부 인공지능응용전략실)
Lee, Seong-Oak (주식회사 TnF.AI)
Kim, Kyoung-Pyo (한국원자력연구원 한사우디원자력공동연구센터)
Yu, Yonggyun (한국원자력연구원 미래전략본부 인공지능응용전략실)
Publication Information
Journal of Korea Society of Industrial Information Systems / v.26, no.5, 2021 , pp. 1-9 More about this Journal
Abstract
Children's drawings are widely used in the diagnosis of children's psychology as a means of expressing inner feelings. This paper proposes a children's drawings-based object detection algorithm applicable to children's psychology analysis. First, the sketch area from the picture was extracted and the data labeling process was also performed. Then, we trained and evaluated a Faster R-CNN based object detection model using the labeled datasets. Based on the detection results, information about the drawing's area, position, or color histogram is calculated to analyze primitive information about the drawings quickly and easily. The results of this paper show that Artificial Intelligence-based object detection algorithms were helpful in terms of psychological analysis using children's drawings.
Keywords
Artificial Intelligence; Deep Learning; Object detection; Image Processing; Child drawing analysis;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Kim, S. K., & Yu, K. (2021). Development of Fuzzy Reasoning based Psychological Diagnosis Application with Automatic Hand-drawing Analysis. Journal of Digital Contents Society, 22(3), 519-525.   DOI
2 Korea Youth Counseling & Welfare Institute (2020). Daily life changed by COVID-19 and Investigation and countermeasures for the perception of adolescent guardians, Youth Counseling Issue Paper, 2, 1-14.
3 Jo, S. H. (2020). [Measures for Prevention of Abuse of Children and Adolescents] Contents and Future Plans, Parenting Policy Forum, 28-33
4 Barak, A. (2011). Internet-based psychological testing and assessment. In: Online Counseling. Academic Press, 225-255.
5 Mattson, D. C. (2015). Usability assessment of a mobile app for art therapy. The Arts in Psychotherapy.
6 Center for Transnational Migration and Social Inclusion (2021). Childcare during Covid-19 and Mental Health Crisis for Parents, Issue Brief Series on Impact of Covid-19 on Care.
7 Yoon, Y. I. (2015). The childrens HTP test application development based on mobile device. Design convergence study, 14, 293-310.
8 Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems, 28, 91-99.
9 Everingham, M., Van Gool, L., Williams, C. K., Winn, J., & Zisserman, A. (2010). The pascal visual object classes (voc) challenge. International journal of computer vision, 88(2), 303-338.   DOI
10 Otsu, N. (1979). A threshold selection method from gray-level histograms. IEEE transactions on systems, man, and cybernetics, 9(1), 62-66.   DOI
11 Howse, J. (2013). OpenCV computer vision with python. Birmingham: Packt Publishing.
12 Park, J., Shin, S., Kim, J. Y., Park, K. H., Lee, S., Jeon, M., Kim, S. (2019). Preliminary Research of HTP Sentiment Analysis Automation on Children's Drawings. The HCI Society of Korea conference, 867-871.
13 Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014). Rich feature hierarchies for accurate object detection and semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition, 580-587.
14 Girshick, R. (2015). Fast r-cnn. In Proce dings of the IEEE international conference on computer vision, 1440-1448.
15 Pizer, S. M., Amburn, E. P., Austin, J. D., Cromartie, R., Geselowitz, A., Greer, T., ... & Zuiderveld, K. (1987). Adaptive histogram equalization and its variations. Computer vision, graphics, and image processing, 39(3), 355-368.   DOI
16 Y. Wu, A. Kirillov, F. Massa, W.-Y. Lo, and Girshick R. (2019). "Detectron2," https://github.com/facebookresearch/detectron2
17 Kolkur, S., Kalbande, D., Shimpi, P., Bapat, C., & Jatakia, J. (2017). Human skin detection using RGB, HSV and YCbCr color models. arXiv preprint arXiv:1708.02694.   DOI