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Using Object Detection Methods to Detect Fashion Trends in University Students’ Attire

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Culture and Computing (HCII 2025)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15800))

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Abstract

With the advancement of technology and the development of artificial intelligence, research methods in fashion design are continuously evolving. This research aims to use object detection techniques to analyze the attire of university students, exploring and uncovering fashion trends. Firstly, we selected YOLOv8 as object detection models, then trained the model and used the training set as well as adjusted parameters to improve accuracy. Secondly, we applied annotation tools to label collected photos, marking different types of clothing (e.g., tops, pants, shoes). To further enhance the quality and variety of the dataset, we leveraged a pre-trained model based on the DeepFashion2 [10] dataset. By leveraging big data and machine learning technologies, this project will establish an automated system capable of analyzing university students’ fashion styles in real-time and with accuracy, providing forward-looking insights for fashion design. In this research, we have chosen to focus on one of the most produced fashion items globally - T-shirts. Mass manufactured knit tops such as T-shirts, polo shirts, and sleeveless tops are chosen as they are one of the most ubiquitous items shown in our preliminary testing, but more importantly, as they are some of the most resource intensive apparel items to produce. In our Dataset creation we create 14 new categories of T-shirt with annotation tool RoboFlow [13] and segmentation technique, which represent different shape, size and color patterns of T-shirts. The students of the Department of Fashion Design and management were the main contributors to this Dataset. Fashion design is not only an art but also a discipline closely related to society and culture. Traditional methods of analyzing fashion trends often rely on surveys and street observations, which are time-consuming and subjective. With the development of artificial intelligence and big data technologies, applying object detection techniques to attire analysis can enhance efficiency and provide more objective and comprehensive data.

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References

  1. Noh, M., Ulrich, P.: Querying fashion professionals’ forecasting practices: the Delphi method. Int. J. Fashion Des. Technol. Educ. 6(1), 63–70 (2013). https://doi.org/10.1080/17543266.2013.765510

    Article  Google Scholar 

  2. Danica, F.-H., Tamara, D., May, D., Meta, N., Mitja, H.F.: Delphi method: strengths and weaknesses. Adv. Meth. Stat. 16(2), 1-19. (2019). https://doi.org/10.51936/fcfm6982

  3. Ray, S.: A quick review of machine learning algorithms. In: 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon), Faridabad, India, pp. 35–39 (2019). https://doi.org/10.1109/COMITCon.2019.8862451

  4. LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015). https://doi.org/10.1038/nature14539

    Article  Google Scholar 

  5. Amjoud, A.B., Amrouch, M.: Object detection using deep learning, CNNs and vision transformers: a review. IEEE Access 11, 35479–35516 (2023). https://doi.org/10.1109/ACCESS.2023.3266093

    Article  Google Scholar 

  6. Sagiroglu, S., Sinanc, D.: Big data: a review. In: 2013 International Conference on Collaboration Technologies and Systems (CTS), San Diego, CA, USA, pp. 42–47 (2013). https://doi.org/10.1109/CTS.2013.6567202

  7. Bailly, A., et al.: Effects of dataset size and interactions on the prediction performance of logistic regression and deep learning models. Comput. Methods Programs Biomed. 213, 106504 (2022). https://doi.org/10.1016/j.cmpb.2021.106504

    Article  Google Scholar 

  8. Luo, C., Li, X., Wang, L., He, J., Li, D., Zhou, J.: How does the data set affect CNN-based image classification performance?. In: 2018 5th International Conference on Systems and Informatics (ICSAI), Nanjing, China, pp. 361–366 (2018). https://doi.org/10.1109/ICSAI.2018.8599448

  9. Steinberg, L.: Risk taking in adolescence: what changes, and why? Ann. N. Y. Acad. Sci. 1021(1), 51–58 (2006). https://doi.org/10.1196/annals.1308.005

    Article  Google Scholar 

  10. Ge, Y., Zhang, R., Wu, L., Wang, X., Tang, X., Luo, P.: A versatile benchmark for detection, pose estimation, segmentation and re-identification of clothing images. CVPR (2019). https://arxiv.org/pdf/1901.07973.pdf

  11. Choi, S., Park, S., Lee, M., Choo, J.: VITON-HD: high-resolution virtual try-on via misalignment-aware normalization. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA, pp. 20–25 June 2021

    Google Scholar 

  12. Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics (2023). https://github.com/ultralytics/ultralytics. Accessed: 30 Feb. 2023

  13. Dwyer, B., Nelson, J., Hansen, T., et. al.: Roboflow (Version 1.0) [Software]. (2024). https://roboflow.com. computer vision

  14. Cartner-Morley, J.: Equal parts practical and daring: how mary quant created look for a new way of living. The Guardian. (2023, April 13). https://www.theguardian.com/fashion/2023/apr/13/equal-parts-practical-and-daring-how-mary-quant-created-look-for-a-new-way-of-living

  15. Atik, D., Cavusoglu, L., Ozdamar Ertekin, Z., Fırat, A.F.: Fashion, consumer markets, and democratization. J. Consum. Behav. 21(5), 1135–1148 (2022)

    Article  Google Scholar 

  16. Kuipers, G., Brans, L., Carbone, L.: The myth of trickle-down: How fashions do (not) spread in European fashion magazines, and what this tells us about power and status in the global fashion system. In: Fashion’s Transnational Inequalities (pp. 114–142) (2023) Routledge

    Google Scholar 

  17. Crofton, S., Dopico, L.: Zara-Inditex and the growth of fast fashion. Essays Econ. Bus. Hist. 25, 41–54 (2007)

    Google Scholar 

  18. Khan, M.M.R., Islam, M.M.: Materials and manufacturing environmental sustainability evaluation of apparel product: knitted T-shirt case study. Text. Clothing Sustain. 1, 1–12 (2015)

    Google Scholar 

  19. DuBreuil, M., Lu, S.: Traditional vs. big-data fashion trend forecasting: an examination using WGSN and EDITED. Int. J. Fashion Des. Technol. Educ. 13(1), 68–77 (2020)

    Google Scholar 

  20. Reilly, A., Hawley, J.: Attention deficit fashion. Fashion Style Popular Cult. 6(1), 85–98 (2019)

    Article  Google Scholar 

  21. Shi, M., Chussid, C., Yang, P., Jia, M., Dyk Lewis, V., Cao, W.: The exploration of artificial intelligence application in fashion trend forecasting. Text. Res. J. 91(19–20), 2357–2386 (2021)

    Article  Google Scholar 

  22. Zahna, M., Douglas, P., Perrin, Ph.D.a,b,*: Efficient human-in-loop deep learning model training with iterative refinement and statistical result validation (2023, April 3)

    Google Scholar 

  23. Long, X., et al.: PP-YOLO: an effective and efficient implementation of object detector. arXiv. (2020). https://doi.org/10.48550/arXiv.2007.12099

  24. Shorten, C., Khoshgoftaar, T.M.: A survey on image data augmentation for deep learning. J Big Data 6, 60 (2019). https://doi.org/10.1186/s40537-019-0197-0

    Article  Google Scholar 

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Acknowledgments

This project is funded by Taiwan Educational Experience Program (TEEP) 2024 from Ministry of Education in Taiwan. We would like to appreciate the active participation of the students of the Department of Fashion Design and Management, National Pingtung University of Science and Technology, Taiwan.

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Authors and Affiliations

  1. Department of Fashion Design and Management, National Pingtung University of Science and Technology, Neipu, Taiwan

    Wei-Her Hsieh

  2. Department of Electrical and Electronics Engineering, International University of Business Agriculture and Technology, Dhaka, Bangladesh

    Md. Rabius Sany Apu

  3. Department of Computer Science and Engineering, Presidency University, Bangaluru, India

    Jatin Thapa

  4. Department of Fashion Design, Binus University, Jakarta, Indonesia

    Owen Kumala

Authors
  1. Wei-Her Hsieh
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  2. Md. Rabius Sany Apu
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  3. Jatin Thapa
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  4. Owen Kumala
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Corresponding author

Correspondence to Wei-Her Hsieh .

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Editors and Affiliations

  1. Eindhoven University of Technology, Eindhoven, The Netherlands

    Matthias Rauterberg

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Hsieh, WH., Apu, M.R.S., Thapa, J., Kumala, O. (2025). Using Object Detection Methods to Detect Fashion Trends in University Students’ Attire. In: Rauterberg, M. (eds) Culture and Computing. HCII 2025. Lecture Notes in Computer Science, vol 15800. Springer, Cham. https://doi.org/10.1007/978-3-031-93160-4_1

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  • DOI: https://doi.org/10.1007/978-3-031-93160-4_1

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