# An Intelligent Solution for Automatic Garment Measurement Using Image Recognition Technologies
## 3 Results
### Obtained Measurement Results  
With accurate segmentation results, which means the garment is accurately separated from the background, we can predict measurements using proposed algorithms for the key point detection (see Figure 6). Table 3 shows the obtained results of garments' measurements providing mean absolute error (MAE). The best accuracy results have been achieved for the dresses with an average of 0.747 cm measurement error, when total length, waist and shoulders dimensions are considered. For dresses, the largest errors are observed in the measurements of the overall length of the dress. However, the predicted waist measurements are very similar to the actual ones, with an average error of only 0.3429 cm. Prediction results of waist dimensions are relatively precise for the skirts as well, because the MAE is 0.421 cm.  
\begin{table}
\begin{tabular}{l c c c c} \hline  & \multicolumn{2}{c}{**With Augmentation**} & \multicolumn{2}{c}{**Without Augmentation**} \\ \hline
**UNet Model** & **MAX Dice** & **AVG Dice** & **MAX Dice** & **AVG Dice** \\ \hline UNet & 0.918 & 0.804 & 0.919 & 0.852 \\ UNet (DeepFashion2) & 0.906 & 0.857 & 0.825 & 0.824 \\ UNet (Carvana) & 0.891 & 0.835 & 0.879 & 0.827 \\ UNet 128 \(\times\) 128 & 0.979 & 0.917 & 0.943 & 0.899 \\ UNet 256 \(\times\) 256 & 0.976 & 0.818 & 0.922 & 0.818 \\ UNet 512 \(\times\) 512 & 0.971 & 0.865 & 0.906 & 0.855 \\ \hline \end{tabular}
\end{table}
Table 2: Different UNet models’ 5 run validation results including maximum and average values of Dice coefficients.  
\begin{table}
\begin{tabular}{l c c c c} \hline  & **Total Length** & **Waist** & **Shoulders** & **Sleeves** & **Average Error** \\ \hline Dresses & 1.113 & 0.343 & 0.783 & - & 0.747 \\ Blazers & 0.903 & - & 1.826 & 0.652 & 1.127 \\ Skirts & 1.650 & 0.421 & - & - & 1.012 \\ \hline \end{tabular}
\end{table}
Table 3: Garment measurement errors given in centimetres.  
More difficulties were encountered in the measurement of blazers dimensions, with the largest errors (MAE = 1.826) predicting the width of shoulders. However, the sleeves are measured quite accurately (MAE = 0.652), even though the same blazers' key point (1) is used in the algorithm. However, it should also be considered that such errors may occur due to measurement inaccuracies, as the algorithm for converting pixels to centimetres with different coefficients gives more accurate results. The highest errors in the measurement of blazers are the most accurate results.  
Figure 5: Dice coefficient value variation during the training process of different UNet architectures (with augmentation and without denoted maximum coefficient values).  
Figure 6: The instances of measurements predicted key points for different types of garments: (**a**) blazer, (**b**) skirt and (**c**) dress.  
the jacket shoulders can also be explained by the complexity of the jacket image set, which includes some cases where the shoulders are difficult to determine due to the small size of the mannequin, and the colour of the jacket, etc. A similar situation is seen with the skirts' length measurements, where about 15% of them have tassels, a translucent top layer, a crooked cut and other issues (see Figure 7).