激光技术2025，Vol.49Issue(2)：166-174,9.DOI:10.7510/jgjs.issn.1001-3806.2025.02.002
面向视障人群的室内视觉辅助算法的研究
Research on indoor visual aid algorithms for visually impaired people
摘要
Abstract
In order to solve the problems of low detection performance,large number of model parameters and difficult deployment in edge devices of the existing indoor vision aided algorithm,the YOLOv7-tiny network was improved and a new YOLOv7-ghost network model was proposed.Firstly,aiming at the problem of large number of model parameters,ghost bottleneck(GB)was introduced to replace partial pooling operation and efficient layer aggregation network(ELAN)to significantly reduce the number of model parameters.Secondly,by constructing a new high-performance lightweight module(C2f-global attention module),the global and local feature information were comprehensively considered to better capture the context information of nodes.Then,spatial pyramid pooling-fast and ghost bottleneck(SPPF-GB)module were introduced to recombine and compress the features to fuse the feature information of different scales and enhance the expression ability of features.Finally,deformable convolution network(DCN)was introduced in the head part to enhance the expression ability of receptive field,so as to capture more fine-grained target structure and background information around the target.The results show that,the parameters of the improved model decrease by 20.33%,the model size decreases by 18.70%,and mean average accuracy mAP@0.50 and mAP@0.50～0.95 increases by 1.2%and 3.3%,respectively.The network model not only ensures lightweight,but also greatly improves the detection accuracy,which is more conducive to the deployment of indoor scene target detection algorithm.关键词
图像处理/轻量化/幽灵瓶颈模块/C2f-全局注意力模块/多尺度特征融合/可变形卷积/YOLOv7-tiny网络模型Key words
image processing/light weight/ghost bottleneck module/C2f-global attention module/multi-scale feature fusion/deformable convolution/YOLOv7-tiny network分类
计算机与自动化引用本文复制引用
欧阳玉旋,张荣芬,刘宇红,彭垚潘..面向视障人群的室内视觉辅助算法的研究[J].激光技术,2025,49(2):166-174,9.基金项目
贵州省基础研究自然科学项目(黔科合基础-ZK[2021]重点001) （黔科合基础-ZK[2021]重点001）
