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

作　　者：欧阳玉旋 张荣芬 刘宇红 彭垚潘 OUYANG Yuxuan;ZHANG Rongfen;LIU Yuhong;PENG Yaopan(College of Big Data and Information Engineering,Guizhou University,Guiyang 550025,China)

机构地区：[1]贵州大学大数据与信息工程学院,贵阳550025

出　　处：《激光技术》2025年第2期166-174,共9页Laser Technology

基　　金：贵州省基础研究自然科学项目(黔科合基础-ZK[2021]重点001)。

摘　　要：为了解决现有室内视觉辅助算法检测性能低、模型参数量大、不易部署于边缘设备等问题,对你只看一次(YOLO)网络YOLOv7-tiny进行改进,提出一种新的YOLOv7-ghost网络模型。针对模型参数量大的问题,引入幽灵瓶颈(GB)代替部分池化操作和高效层聚合网络(ELAN),大幅度降低模型参数量;构建了一个全新的高性能轻量化模块(即C2f-全局注意力模块),综合考虑全局和局部特征信息,更好地捕捉节点的上下文信息;然后引入快速空间金字塔池化和幽灵瓶颈(SPPF-GB)模块,对特征进行重组和压缩,以融合不同尺度的特征信息、增强特征的表达能力;最后在头部引入可变形卷积(DCN),增强感受野的表达能力,以捕获目标周围更细粒度的目标结构和背景信息。结果表明,改进后的模型参数量下降了20.33%,模型大小下降了18.70%,平均精度mAP@0.50和mAP@0.50~0.95分别提升了1.2%和3.3%。该网络模型在保证轻量化的同时,检测精度得到了大幅度的提升,更利于室内场景目标检测算法实际应用的部署。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 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术] R339.14[自动化与计算机技术—计算机科学与技术]