An Intelligent Solution for Automatic Garment Measurement Using Image Recognition Technologies

Abstract

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. The main challenge is related to the extraction of garment information-general clothing descriptions and automatic dimensional extraction. In this paper, we propose the garment measurement solution based on image processing technologies, which is divided into two phases, garment segmentation and key points extraction. UNet as a backbone network has been used for mask retrieval. Separate algorithms have been developed to identify both general and specific garment key points from which the dimensions of the garment can be calculated by determining the distances between them. Using this approach, we 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 skirts.

Deep-GD: Deep Learning based Automatic Garment Defect Detection and Type Classification

“…Because so many mistakes can be made while processing clothing manually, it can be challenging. Human inspection does not take defect correlation into account when finding defects [8,9]. The assembly line balancing problem affects sewing lines because of human differences in abilities and efficiency.…”

Special Issue “Advanced Digital Technologies for the Integration of Production and Maintenance”

“…The presented procedure showed that the implementation of a proper decision-making process requires the constant flow of information exchange between the actors involved, aiming at providing a complete and detailed overview of the operating context. Paulauskaite-Taraseviciene et al [4] presented a methodology based on image processing technologies for automated data extraction. This approach involves two phases-object segmentation and key points extraction-and it is applied to automatically measure the hanging garments disregarding constraints such as space restriction, background requirements, shooting distances, or additional tags needed for measurements.…”

Intelligent Measurement of Frontal Area of Leaves in Wind Tunnel Based on Improved U-Net

“…Yu performed intelligent measurement of fish morphological characteristics based on U-Net [37]. Paulauskaite-Taraseviciene measured garments automatically based on U-Net [38]. Li measured the body shapes of goats and cattle under different backgrounds based on U-Net [39].…”