# An Intelligent Solution for Automatic Garment Measurement Using Image Recognition Technologies
## 1 Introduction  
One of the most powerful and widely used types of artificial intelligence is computer vision, which aims to mimic some of the complexity of the human visual system and enable computers to detect and identify objects in images and videos. Computer vision techniques cover an increasing number of applications and engineering aspects of computing related to image recognition, including scientific work proposing innovative algorithms or solutions for commercial, industrial, military and biomedical applications. The increasing use of computer vision in everyday life contributes to the efficiency of various aspects of the field. Although the use of these technologies allows solving many complex tasks (automated object detection and identification, tracking), the detection of defects and anomalies is one of the most valuable investigations in medicine [1], bio-medicine [2], manufacturing [3] and agriculture [4]. For example, image recognition techniques based on deep learning can be used to enable advanced disease control in agriculture [5; 6; 7], to identify product defects and increase the quality control in manufacturing [8], automated assessment, prediction and assistance in medicine [9; 10], increase the success rate of bio-medicine procedures [11], provide intelligent road safety solutions [12; 13] and many others. Online shopping is the most popular online activity worldwide, and the key value of intelligent image recognition solutions for e-commerce lies in the ability to identify products quickly and accurately. However, global digitization trends and the application of high technology in the garment market are still too slow to integrate, despite the increasing demand for automated solutions and the fact that the challenges are quite clear and already discussed in different researches. In principle, the main challenge is related to the extraction of garment information-general clothing descriptions, automatic dimensional extraction and textual information retrieval from the tags (size, brand, fabric composition, etc.). Currently, the accuracy and completeness of the information about garments on salesplatforms still relies on a significant amount of manual and tedious work. Measuring a garment is extremely time-consuming and often requires multiple measurements to reduce measurement error. Artificial intelligence (AI) technologies have the potential to meet the demand to adopt the automation technology in this sector by increasing the speed and accuracy of garment measurement [14; 15; 16]. Given CNN's success in a range of domains, the deep learning-based solution has also demonstrated its superiority in performing a variety of garment recognition tasks. The approach based RCNN has been proposed for the shirt attributes recognition task, including the Inception-ResNet V1 model with LSoftmax for images representation and identification of their categories [17]. The experimental results show an overall labelling rate of 87.77%, a precision of 73.59% and a recall of 83.84%. A fully convolutional network and SP-FEN architecture have been proposed to parse clothing in fashion images. The proposed model has shown accuracy in terms of the overall pixel-wise accuracy and clothes parsing performance (pixel accuracy of 92.67 and MIoU of 48.26) [18]. However, the main objective is to identify individual garments, which implies a semantic segmentation task by assigning a class label to each pixel of the image. A review of relevant research has shown that it is much easier to measure clothes lying down than hanging (e.g., on a mannequin). In [19], special equipment to capture images of tiled garments has been proposed which enables automatic garment measurements. The shooting device consists of a digital camera, LED light, shooting stand and workbench. A garment template is employed to recognize garment types and feature points, which are used to calculate garment sizes.