关键词: 边缘检测, 卷积神经网络（CNN）, Transformer, 多模态, 深度学习

Abstract: Edge detection plays a crucial role in computer vision tasks. However, existing edge detection algorithms primarily rely on CNN as encoders, which results in defects in terms of precision, accuracy, and noise handling. To address these issues, a multi-modal edge detection network based on the collaboration of CNN and Transformer is proposed. Firstly, the network employs a high-resolution feature fusion module based on a non-parametric attention residual structure, which retains the low-level properties of the image and enhances global feature representation. Next, a lightweight CNN layer with a multi-scale shuffle attention module is designed to perform gradient encoding, capturing the high-frequency properties of the image. The Transformer architecture is employed to achieve feature encoding and build high-level global dependencies. Then, the features are reconstructed by fusing the high-frequency properties and global dependencies. Finally, multi-scale features from the CNN, Transformer, and high-resolution feature fusion module are progressively aggregated and decoded, enabling high-precision localization of image boundaries. Compared to mainstream algorithms, the proposed model achieves superior metrics on both the BSDS500 and NYUD-v2 datasets.

Key words: edge detection, convolutional neural network (CNN), Transformer, multi-model, deep learning