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输电线巡检机器人弱光条件下的障碍物识别研究

Obstacle Identification Under Low-Light Conditions of Transmission Line Inspection Robot

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摘要

在室外光照条件变化下进行有效的障碍物识别是高压输电线路巡检机器人所面临的技术难点之一。针对弱光条件下障碍物识别的稳健性问题,提出了一种基于机器人视觉的障碍物识别智能方法,以使巡检机器人适应各种不同程度的弱光变化。通过对采集的障碍物图像进行自适应同态滤波处理,以减少部分光照的影响;将障碍物图像分成均匀大小的子区域,运用改进的局部方向模式提取各个子区域图像的特征直方图向量,并把子块特征直方图逐个串联为总的直方图;再选用卡方距离法进行统计识别。实验结果表明:该方法使巡检机器人对输电线上的防震锤、悬垂线夹和绝缘子串能够进行有效的识别。相比于其他算法,其具有更好的抗光照干扰效果和更高的准确识别率;提升了机器人巡检过程中图像识别的稳健性、适应性和准确性,极大地提高了巡检机器人在电力行业的可持续发展性。

Abstract

It is one of the technical difficulties for the inspection robot of high voltage transmission line to identify obstacles effectively under the changes of outdoor lighting conditions. According to the robustness problem of obstacle identification under the low-light conditions, an intelligent method of obstacle recognition based on robot vision is put forward, so that the inspection robot can deal with various degrees of low-light changes. The obstacle images are processed by self-adaptive homomorphic filtering to reduce the influence of illumination partially. A obstacle image is divided into uniform sub-regions. The improved local direction pattern is used to extract the feature histogram vector of each sub-region image, and the sub-block feature histograms are concatenated one by one into the total histogram. The Chi distance is used to perform statistical identification. The experimental results show that this method can make the inspection robots effectively recognize the counterweight, suspension clamp and insulator string on the transmission line. Compared with other algorithms, the improved local directional pattern has a better anti-light interference effect and higher accurate recognition rate, and improves the robustness, adaptability and accuracy of image recognition during robot inspection. It greatly promotes the sustainability of inspection robots in the power industry.

Newport宣传-MKS新实验室计划
补充资料

中图分类号:TP24

DOI:10.3788/aos201838.0915006

所属栏目:机器视觉

基金项目:广东省机器人专项(2015B090922007)、广东省佛山市科技创新团队项目(2015IT100143)

收稿日期:2018-03-27

修改稿日期:2018-04-27

网络出版日期:2018-05-02

作者单位    点击查看

黄乐:武汉大学动力与机械学院, 湖北 武汉 430072
吴功平:武汉大学动力与机械学院, 湖北 武汉 430072
叶旭辉:武汉大学动力与机械学院, 湖北 武汉 430072

联系人作者:吴功平(gpwu@whu.edu.cn)

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引用该论文

Huang Le,Wu Gongping,Ye Xuhui. Obstacle Identification Under Low-Light Conditions of Transmission Line Inspection Robot[J]. Acta Optica Sinica, 2018, 38(9): 0915006

黄乐,吴功平,叶旭辉. 输电线巡检机器人弱光条件下的障碍物识别研究[J]. 光学学报, 2018, 38(9): 0915006

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