联合SIFT-GPU和整体平差法的隧道影像拼接研究

张栋樑; 宫志群; 杨世廷; 陆业宁; 占静; 安晓亚

doi:10.3973/j.issn.2096-4498.2023.07.004

隧道建设 >

2023 , Vol. 43 >Issue 7: 1118 - 1126

DOI: https://doi.org/10.3973/j.issn.2096-4498.2023.07.004

研究与探索

联合SIFT-GPU和整体平差法的隧道影像拼接研究

张栋樑 ,
宫志群 ,
杨世廷 ,
陆业宁 ,
占静 ,
安晓亚

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（1. 中国建设基础设施有限公司, 北京 100029； 2. 武汉天际航信息科技股份有限公司, 湖北武汉 430074； 3. 西安测绘研究所，陕西西安 710054； 4. 地理信息工程国家重点实验室，陕西西安 710054）

张栋樑（1979—），男，黑龙江牡丹江人，2008年毕业于同济大学，道路与铁道工程专业，博士，高级工程师，现从事道路工程技术管理工作。Email： 466716578@qq.com。*通信作者：占静， Email： zhanjing@whulabs.com。

网络出版日期: 2023-08-06

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Tunnel Image Stitching Based on ScaleInvariant Feature TransformGraphics Processing Unit and Integral Adjustment Method

ZHANG Dongliang ,
GONG Zhiqun ,
YANG Shiting ,
LU Yening ,
ZHAN Jing ,
AN Xiaoya

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(1. China Construction Infrastructure Co., Ltd., Beijing 100029, China; 2. Wuhan Tianjihang Information Technology Co., Ltd., Wuhan 430074, Hubei, China; 3. Xi′an Research Institute of Surveying and Mapping, Xi′an 710054, Shaanxi, China; 4. State Key Laboratory of Geoinformation Engineering, Xi′an 710054, Shaanxi, China)

Online published: 2023-08-06

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摘要

针对当前隧道影像拼接过程中效率低、累积误差大的问题，研究提出一种联合SIFT-GPU（scale-invariant feature transform-graphics processing unit）协同处理算法和整体平差法的隧道影像拼接技术。首先，基于车载移动测量技术获取隧道内部的影像和点云数据，利用点云数据和共线方程对原始影像进行纠正处理，并先后使用MASK匀光算法和Wallis滤波匀光法对影像进行匀光匀色；然后，融合SIFT匹配算法和GPU-CPU（central processing unit）协同处理技术加速提取所有待拼接影像特征点，使用RANSAC（random sample consensus）算法剔除误配点；最后，使用平移旋转缩放变换模型对400张影像进行拼接，采用整体平差算法对拼接结果进行误差补偿,利用GDAL（geospatial data abstraction library）分块处理技术输出拼接成果。研究结果表明： 1)相较于原始的SIFT匹配算法，研究使用的SIFT-GPU协同处理算法使影像匹配效率提高了3~25倍； 2)整体平差算法较好地解决了随着待拼接影像的增加而引起的误差累积问题，相较于传统算法，误差降低至原来的1/3。

关键词： 隧道; 影像拼接; SIFT; RANSAC; 整体平差法

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张栋樑 , 宫志群 , 杨世廷 , 陆业宁 , 占静 , 安晓亚 . 联合SIFT-GPU和整体平差法的隧道影像拼接研究[J]. 隧道建设, 2023 , 43(7) : 1118 -1126 . DOI: 10.3973/j.issn.2096-4498.2023.07.004

Abstract

The existing tunnel image stitching is inefficient and prone to large cumulative errors. Therefore, a technology for tunnel image stitching based on a collaborative processing algorithm of scaleinvariant feature transform(SIFT)graphics processing unit(GPU) and integral adjustment method is required. First, the image and point cloud data inside the tunnel are obtained using vehiclemounted mobile measurement technology; the original image is corrected using point cloud data and collinear equation, and the light and color are homogenized using the MASK smoothing algorithm and Wallis filter smoothing method. Then, the SIFT matching algorithm is integrated using a collaborative processing technology of the GPUcentral processing unit to extract feature points of all images to be stitched, and the random sample consensus algorithm is used to remove mismatches. Finally, a translation rotation scaling model is used to stitch 400 images, the global adjustment algorithm is used to compensate for the error, and the block processing technology of the geospatial data abstraction library is used to output the results. According to the experimental findings, the collaborative processing algorithm of SIFTGPU can improve imagematching efficiency by 3 to 25 times compared to the original SIFT matching algorithm. Compared to the original algorithm, the integral adjustment algorithm can more effectively address the error accumulation problem caused by the increase of the image to be stitched, resulting in a threefold reduction in error.

Key words： tunnel; image stitching; scaleinvariant feature transform; random sample consensus; integral adjustment

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