控制与信息技术Issue(6)：88-95,8.DOI:10.13889/j.issn.2096-5427.2024.06.012
基于自适应阈值与多特征融合的脉冲神经网络语义分割方法
SNN-Based Semantic Segmentation Method Using Adaptive Threshold and Multi-Feature Fusion
摘要
Abstract
Spiking neural networks(SNN)are asynchronous and sparse,making them a low-power alternative to artificial neural networks(ANN)in specific scenarios.However,SNNs exhibit deficiencies such as limited feature expression and low result accuracy when utilized for complex tasks such as semantic segmentation.To address this issue,this paper proposes a SNN-based semantic segmentation methodology using adaptive thresholds for spike triggering and multi-feature fusion.First,input images are encoded utilizing adaptive threshold neurons for spiking,and the spiking visual geometry group(VGG)is utilized for spiking feature extraction from the encoded results.To further improve the network's ability of feature extraction for spike sequences,a spiking feature fusion(SFF)module designed at the encoder end subsequently functions to perform multi-scale feature extraction and fusion of the spike sequences.In the final decoding stage,the spike sequences are stacked in the time dimension,and spiking features at the encoding and decoding layers are fused to improve the network's capability in boundary feature extraction.Experimental results showed that the proposed approach achieved a mean intersection of union(mIoU)on the VOC2012 dataset that was 6.8%higher than that of existing SNN semantic segmentation algorithms.This solution also yielded good segmentation effect on the OCS dataset in high-speed railway scenarios.Additionally,the proposed method consumed less energy compared to ANN-based semantic segmentation algorithms.关键词
脉冲神经网络/自适应阈值/语义分割/特征融合/脉冲序列Key words
spiking neural network(SNN)/adaptive threshold/semantic segmentation/feature fusion/spike sequence分类
信息技术与安全科学引用本文复制引用
黄永斌,李晨,董文波,刘顺莲..基于自适应阈值与多特征融合的脉冲神经网络语义分割方法[J].控制与信息技术,2024,(6):88-95,8.基金项目
新一代人工智能国家科技重大专项(2021ZD0109805) （2021ZD0109805）
湖南省教育厅优秀青年项目(NO.23B0569) （NO.23B0569）
