Feature fusion method based on spiking neural convolutional network for edge detection.

NSNP-type neuron is a new type of neuron model inspired by nonlinear spiking mechanisms in nonlinear spiking neural P systems. In order to address the loss problem of edge detail information in edge detection methods based on deep learning, we propose a feature fusion method based on NSNP-type neurons. The architecture of this feature fusion method consists of two modules: feature extraction module and feature fusion module. In particular, the feature fusion module is composed of convolutional blocks constructed by NSNP-type neurons for multi-level feature fusions, and CoT blocks with Transformer style is introduced to extract rich contextual information from low-level features and high-level features. To fuse multi-level features and preserve contextual information, we design a new loss function that not only preserves feature prediction loss and fusion loss, but also considers contour-related and texture-related information. The proposed method is evaluated on BSDS500 and NYUDv2 data sets and compare it with 9 baseline methods and 12 CNN-based methods, and we achieve ODS of 0.808 and OIS of 0.827 on BSDS500. The comparison results demonstrate the advantages of the proposed method for edge detection. The source code is available at https://github.com/xhuph66/FF-CNSNP-master. • Nonlinear mechanism in spiking neurons inspires the NSNP-type neuron model. • NSNP-like neuron model is used to construct a feature fusion module for fusing edge feature maps. • This feature fusion module can effectively fuse the global and local information of the image. • A new loss function is designed to consider information related to contours and textures. [ABSTRACT FROM AUTHOR]