输电线路除冰机器人基于小波矩及SVM的障碍物识别研究  

Research on Obstacle Recognition Based on Wavelet Moments and SVM for Deicing Robot on High Voltage Transmission Line

作　　者：曹文明[1] 王耀南[1] 文益民[2] 

机构地区：[1]湖南大学电气与信息工程学院,湖南长沙410082 [2]桂林电子科技大学计算机科学与工程学院,广西桂林541004

出　　处：《湖南大学学报（自然科学版）》2012年第9期33-38,共6页Journal of Hunan University:Natural Sciences

基　　金：国家支撑计划资助项目(2008BAF36B01);国家863项目资助(2008AA04Z214)

摘　　要：障碍物检测与识别是高压输电线路除冰机器人自主越障和行走的前提条件.本文根据除冰机器人自身结构以及高压线路工作环境的要求,设计了一种障碍物智能识别方法.首先对机器人在线拍摄的障碍物图像进行预处理、最佳阈值处理,然后用小波模极大值计算二值图像边缘,再计算具有不变性的图像小波矩,把优化后的小波矩特征输入支持向量机(SVM)进行分类,从而实现对障碍物的识别.实验表明:障碍物的小波矩特征向量稳定可靠,SVM目标识别准确率高,利用两者优势对障碍物进行识别是一种切实可行的方法。Obstacle recognition is one of the key techniques for deicing robot on high voltage transmission line. According to the structure of 220 kV transmission line and the feature of special environment, the images photo- graphed on line by the robot camera can be processed. Images have been binary converted on the basis of threshold op- timization after some preprocesses of de-noising, expansion and erosion. Then, image edges were detected by using wavelet modulus maximum algorithm, and the wavelet moments of edge images that have invariance were calculated. The SVM begins to classify images after being put into eigenvectors that have been optimized into the neural network of SVM, which realizes the image classification of obstacle. Simulation experiments have indicated that eigenvectors of wavelet moments are stable and reliable and SVM classifier has a high accuracy of object recognition. It is a feasible method that combines both virtues.

关 键 词：障碍物检测 除冰机器人 小波模极大值算法 SVM分类器 

分 类 号：TP24[自动化与计算机技术—检测技术与自动化装置]