# An Intelligent Solution for Automatic Garment Measurement Using Image Recognition Technologies
## 5 Conclusions  
This paper addresses the problem of automatic measurements of garments dimensions. The proposed solution consists of deep learning-based garment segmentation and the detection of key points needed to measure the main garment dimensions. Different UNet family architectures have been employed for segmentation tasks. The UNet 128 \(\times\) 128 model with a Dice accuracy of 0.977 has demonstrated the highest accuracy results compared with other UNet models and showed the superiority over the UNet models pre-trained with the additional datasets. The key points detection process has been performed on the predicted masks obtained using the Unet 128 \(\times\) 128 model. Separate algorithms (for the blazers, skirts and dresses) have been developed in this research to identify general and specific garment key points enabling us to measure the dimensions of the garment. Automatic measurements experiments including three types of garments (blazers, skirts and dresses) have resulted in an average 1.27 cm measurement error for the prediction of the basic measurements of blazers, 0.747 cm for dresses and 1.012 cm for the skirts. The results are promising, given that in the industry a measurement error of up to \(\sim\)2 cm is acceptable, while human measurement error can be up to 3.02. The comparison of existing solutions is quite difficult due to the purpose of the proposed solution itself, diverse environmental conditions, individual datasets or evaluation metrics. Commercial solutions currently available on the market aim to make the process of purchasing clothes easier while reducing the number of returns. Therefore, the first and most important step is to obtain accurate body measurement data by integrating deep learning algorithms and other artificial intelligence techniques. The second objective of  
Figure 9: The error of manual measurement for two different types of clothes: skirts and men’s jackets. such systems (mobile apps) is to provide personalized clothing sizing recommendations to help eliminate sizing problems. Such systems can be called smart shopping assistants with quite clear objectives, including sustainability. The solution proposed in this study focuses on the automated extraction of garment information, i.e., the recognition of the type of garment and the accurate measurement of its dimensions, without being tied to the positioning of the garment (lying down, on a mannequin or on a hanger). One of the key goals of this research is to enable less-standardized garment photography, in contrast to current garment measurement systems which require fixed position setup, calibration, and/or dedicated infrastructure to ensure small error (\(\sim\)0.318 cm). Although the quality of the photos is certainly important, our solution does not require the highest quality professional photos, so it can be used both in the industry and on online platforms selling second-hand or new clothes (e.g., \"Ebay\", \"Vinted\"). The results presented in this study are related to the most important and challenging aspect of garment information identification--automated garment dimension measurement. However, the potential for extending such a solution is significant. A further objective is to automate the manual entering of all the information about the garment, such as what is the type, colour, size, etc. Moreover, additional solutions could be added to retrieve information from the label, which includes information on fabric composition, garment size and brand. Colour identification is one of the simplest tasks, but it is possible to develop a more sophisticated solution based on unsupervised learning algorithms to automatically identify a few dominant colours (in the case of a multi-coloured or patterned garment), and incorporate an adaptive and broad palette of colours, rather than a fixed and narrowly defined range of colours. Conceptualization, A.P.-T.; data curation: R.S.-Z., A.P.-T., I.L.-B. and E.N. ; investigation, A.P.-T., V.D., E.N. and R.P. ; methodology, A.P.-T., I.L.-B., E.N. and R.P.