计算机技术与发展2025，Vol.35Issue(7)：1-7,7.DOI:10.20165/j.cnki.ISSN1673-629X.2025.0041
改进FlowNetS的光流估计算法研究
Advanced Research on Improving Optical Flow Estimation Based on FlowNetS
摘要
Abstract
Deep learning-based methods for optical flow estimation have shown remarkable progress in recent years,achieving notable gains in both accuracy and efficiency.However,these methods continue to suffer from several shortcomings,including a strong dependency on extensive training datasets,sensitivity to specific environmental conditions,high computational costs,a lack of effective in-corporation of physical constraints,and limited interpretability.To address these issues,we propose additive fusion methodology using convolutional neural networks(CNNs)to enhance the representational capacity of optical flow features,improve the accuracy of predictions,and simultaneously reduce memory requirements.We specifically introduce a novel stacked fusion module,termed the Continue-ADD-Block.This module effectively consolidates multi-scale information through the inclusion of a Conv7 layer and the ap-plication of consecutive downsampling for the fusion process.This integration process strengthens the model's capacity to cope with complex scenes characterized by multi-scale motion.Empirical evaluations conducted on the Flying Chairs,KITTI Flow 2015,and MPI-Sintel(both clean and final versions)datasets,demonstrate that the Continue-ADD-BLOCK achieves superior accuracy(expressed as a lower maximal average End-Point Error(EPE))in complex scenarios.Critically,this performance gain is achieved while simultaneously reducing resource consumption and the memory footprint.Specifically,the approach demonstrated accuracy improvements of 2.86％on Flying Chairs,0.70％on KITTI Flow 2015,and 7.40％and 2.43％on the MPI-Sintel(clean and final)datasets,respec-tively.These findings highlight the proposed method's enhanced robustness under challenging circumstances,thus offering a novel and effective approach for optical flow estimation.关键词
光流估计/分层策略/多尺度估计/叠加融合/卷积神经网络Key words
optical flow estimation/hierarchical strategy/multi-scale estimation/additive fusion/convolutional neural network分类
信息技术与安全科学引用本文复制引用
王雅妮,翟正军,代巍,申思远..改进FlowNetS的光流估计算法研究[J].计算机技术与发展,2025,35(7):1-7,7.基金项目
陕西省教育科学研究计划项目(21JK0684) （21JK0684）
工信部基金优选项目(MJZ1-8N22) （MJZ1-8N22）
