Cloth manipulation based on category classification and landmark detection

Abstract

Cloth manipulation remains a challenging problem for the robotic community. Recently, there has been an increased interest in applying deep learning techniques to problems in the fashion industry. As a result, large annotated data sets for cloth category classification and landmark detection were created. In this work, we leverage these advances in deep learning to perform cloth manipulation. We propose a full cloth manipulation framework that, performs category classification and landmark detection based on an image of a garment, followed by a manipulation strategy. The process is performed iteratively to achieve a stretching task where the goal is to bring a crumbled cloth into a stretched out position. We extensively evaluate our learning pipeline and show a detailed evaluation of our framework on different types of garments in a total of 140 recorded and available experiments. Finally, we demonstrate the benefits of training a network on augmented fashion data over using a small robotic-specific data set.

Research on the Clothing Classification of the She Ethnic Group in Different Regions Based on FPA-CNN

“…Wu et al [17] used semantic attributes and multi-task learning to recognize 25 ethnic minority clothing groups in Yunnan, and the recognition accuracy reached 82.5~88.4%. Currently, research on clothing recognition mainly focuses on different types of clothing styles [18][19][20], such as upper garments and lower garments or clothing styles with significant differences in contours [21][22][23]. Recognizing similar styles with small differences in the same category poses a challenge.…”