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基于Order-Aware网络内点筛选网络的电力巡线航拍图像拼接
引用本文:回立川,李万禹,陈艺琳.基于Order-Aware网络内点筛选网络的电力巡线航拍图像拼接[J].计算机应用,2022,42(5):1583-1590.
作者姓名:回立川  李万禹  陈艺琳
作者单位:辽宁工程技术大学 电气与控制工程学院,辽宁 葫芦岛 125105
基金项目:辽宁省教育厅科学研究项目(LJ2017QL009)~~;
摘    要:电力巡线图像纹理复杂且具有视差变化,针对传统算法获取成对匹配点数量较少、配准精度较低,严重影响电力巡线无人机图像拼接效果等问题,提出了一种基于改进OANet的图像拼接算法。首先,借助加速“风”(AKAZE)算法对待拼接电力巡线图像进行粗匹配;其次,对OANet中Order-Aware模块添加挤压和激励网络(SENet),从而增强网络对局部和全局上下文信息的抓取能力,得到更精确的成对匹配点;然后,通过MPA算法配准待拼接图像;最后,借助内容压缩感知算法计算重叠区域的最佳缝合线以完成图像拼接。改进OANet相较原OANet的正确匹配点数量增加了10%左右,耗时平均增加了10 ms;与APAP算法、AANAP算法、MPA算法等配准拼接算法相比,所提算法的拼接质量最好,其待拼接图像的重叠区域的均方根误差为0,非重叠区域未发生畸变。实验结果表明,所提算法可快速、稳定地拼接电力巡线航拍图像。

关 键 词:电力巡线  图像拼接  OANet  挤压和激励网络  MPA算法  内容压缩感知算法  
收稿时间:2021-04-01
修稿时间:2021-05-18

Power line inspection aerial image stitching based on Order-Aware network internal point screening network
Lichuan HUI,Wanyu LI,Yilin CHEN.Power line inspection aerial image stitching based on Order-Aware network internal point screening network[J].journal of Computer Applications,2022,42(5):1583-1590.
Authors:Lichuan HUI  Wanyu LI  Yilin CHEN
Affiliation:Faculty of Electrical and Control Engineering,Liaoning Technical University,Huludao Liaoning 125105,China
Abstract:The texture of power line inspection images with parallax variation is complex, the number of paired matching points obtained by traditional algorithms is less and the registration accuracy is low, which seriously affect the stitching effect of power line inspection unmanned aerial vehicle image. In order to solve the problems, a new image stitching method based on improved Order-Aware Network (OANet) was proposed. Firstly, the Accelerated KAZE (AKAZE) algorithm was adopted to match the power line inspection images to be stitched roughly. Secondly, the Squeeze-and-Excitation Networks (SENet) was added to the Order-Aware module in OANet, which helped to enhance the grasping ability of the network for both the local and global context information, and more accurate paired matching points were obtained. Then, the Mesh-based Photometric Alignment (MPA) algorithm was used to register the images to be stitched. Finally, the optimal suture line in the overlapping area was calculated by the content compressed sensing algorithm to complete image stitching. The number of correct matching points of the improved OANet network is about 10% higher than that of the original OANet network with time consumption increased by 10 ms on average. Compared with the registration stitching algorithms such as As-Projective-As-Possible (APAP) algorithm, Adaptive As-Natural-As-Possible (AANAP) algorithm and MPA algorithm, the proposed algorithm has the highest stitching quality with the root mean square error of the overlapping area of the images to be stitched is 0 and no distortion in the non-overlapping area. Experimental results show that, the proposed algorithm can stitch the aerial images of power line inspection quickly and stably.
Keywords:power line  inspection image stitching  Order-Aware Network (OANet)  Squeeze-and-Excitation Network (SENet)  Mesh-based Photometric Alignment (MPA) algorithm  content compressed sensing algorithm  
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