Design and Implementation of A Laundry Folding Automation System Based on Deep Learning 


Vol. 14,  No. 8, pp. 588-594, Aug.  2025
https://doi.org/10.3745/TKIPS.2025.14.8.588


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  Abstract

This study designed an automated system to improve household labor efficiency by automating clothing recognition and folding tasks. A CNN model was trained on the Fashion MNIST dataset to classify tops, and an OV7670 camera sensor was used to recognize clothing. Based on the classification results, the folding points were predicted, and an algorithm was implemented to automate the folding process. The study found that the CNN model effectively classified the clothing, and the folding algorithm successfully performed tasks according to preset rules. These findings suggest that integrating computer vision and automation can significantly reduce household labor, providing a foundation for future smart home applications.

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[IEEE Style]

K. E. Sun and S. Jin-Gon, "Design and Implementation of A Laundry Folding Automation System Based on Deep Learning," The Transactions of the Korea Information Processing Society, vol. 14, no. 8, pp. 588-594, 2025. DOI: https://doi.org/10.3745/TKIPS.2025.14.8.588.

[ACM Style]

Kim Eun Sun and Son Jin-Gon. 2025. Design and Implementation of A Laundry Folding Automation System Based on Deep Learning. The Transactions of the Korea Information Processing Society, 14, 8, (2025), 588-594. DOI: https://doi.org/10.3745/TKIPS.2025.14.8.588.

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