Automatic Recognition of Garment Illustrations Based on CNN

In the process of garment production, obtaining and identifying garment illustrations, transforming them into the required information, and then implementing the information into automated production can improve the production efficiency to a great extent. However, the research on recognition of garment illustration and pattern image is mostly based on category classification, but very little on the identification of parts and details. The Inception module in the GoogleNet Inception and its improvement development models enhance parameter utilization, accelerate computation, and have no special requirements for hardware. The Mask R-CNN, a convolutional neural network, is a modified model from based on Faster R-CNN for instance splitting tasks. Based on these two models, this paper proposes a method to identify garment illustrations using a self-built database. The experimental results show that this method outperforms the related algorithms.