Neural network enhanced automatic garment measurement system

The measurement of garments is most often a verylaborious task. Automatic garment measurement systems may be thus a great convenience in fashion e-commerce cataloguing issues. In this paper, we propose an automatic garment measurement system that uses classical computer vision algorithms, as well as an error correction neural network, which reduces the overall error. We make use of data collected by our partner, which contains photographs of garments with ArUco markers. Using such data, we estimate the coordinates of feature points, which are used to calculate a specific size of a garment. We apply the error correction neural network to this measured size to minimize the error. The conducted experiments show, that our method is a useful tool that meets the requirements of practicality and its results are comparable with the current state of the art methods. Additionally, our error correction neural network is a novelty in the field of automatic garment measurement and there is no need for the garments templates, which are used in the previous solutions.

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Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).