Multi-Scale Tensorial Summation and Dimensional Reduction Guided Neural Network for Edge Detection

Edge detection has garnered considerable attention because of its exceptional ability to enhance performance in downstream computer vision tasks. In recent years, various deep learning methods have been explored for edge detection tasks, yielding significant performance improvements compared to conventional computer vision algorithms. In neural networks, edge detection tasks require considerably large receptive fields to provide satisfactory performance. In a typical convolutional operation, such a large receptive field can be achieved by utilizing a significant number of consecutive layers, which yields deep network structures. Recently, a Multi-scale Tensorial Summation (MTS) factorization operator was presented, which can achieve very large receptive fields even from the initial layers, In this paper, we propose a novel MTS Dimensional Reduction (MTS-DR) module guided neural network, MTS-DR-Net, for the edge detection task The MTS-DR-Net utilizes the MTS layers and the corresponding MTS-DR blocks as a new backbone to remove redundant information initially. Such a dimensional reduction module enables the neural network to focus specifically on relevant information (i.e., necessary subspaces). Finally, a weight U-shaped refinement module follows MTS-DR blocks in MTS-DR-Net We conducted extensive experiments on two benchmark edge detection datasets: BSDS500 and BIPEDv2, to verify the effectiveness of our model. The implementation of the proposed MTS-DR-Net can be found in https://github.com/LeiXuAI/MTS-DR-Net.git.