Image segmentation methods have been used
in the field of recognition and segmentation of traditional embroidery
patterns. However, due to the characteristics of complex edges, cumbersome
details and diverse types of embroidery patterns, traditional image
segmentation methods are difficult to meet the requirements of high precision
and high efficiency in practical applications. So, this
paper presents a dual-stage embroidery pattern recognition and segmentation
method based on a YOLO and U-Net cascade. In the first stage, the YOLO
algorithm is employed for object detection, quickly and accurately locating the
embroidery patterns within the image. In the second stage, an enhanced U-Net
algorithm is used for semantic segmentation. The U-Net encoder structure is
improved by incorporating a ResBlock-CBAM module as the backbone, enhancing the
effectiveness of feature extraction, and integrating an ASPP module for feature
enhancement to ensure effective extraction and fusion of various features. This
dual-stage cascade network captures the fine details and contextual information
of embroidery images, enabling precise segmentation that preserves complex
edges and details. The experimental results show that the algorithm in this
study reaches 0.8584 and 0.8376 in the evaluation indicators such as Dice and
MioU, respectively, and the accuracy rate reaches 84.53%, which is
significantly better than other advanced segmentation algorithms. At the same
time, this paper establishes an "embroidery intelligent recognition and segmentation"
system to achieve efficient and automatic extraction and processing of
embroidery patterns. This method not only provides technical support for the
digital preservation and transmission of embroidery patterns, but also paves
the way for the modernization and customization of embroidery design.