Transmission Line Obstacle Detection Based on Structural Constraint and Feature Fusion

Xuhui Ye,Xinyu Hu,Dong Wang,Daode Zhang

doi:10.3390/sym12030452

Abstract

Accurate detection and identification of obstacles plays an important role in the navigation and behavior planning of the patrol robot. Aiming at the patrol robot with camera mounted symmetrically, an obstacle detection method based on structural constraint and feature fusion is proposed. Firstly, in order to discover the region of interest, the bounding box algorithm is used to propose the region. The location of the detected ground wire is used to constrain the region, and the image block of interest is clipped. Secondly, in order to accurately represent the multi-view and multi-scale obstacle images, the global shape features and the improved local corner features are fused by different weights. Then, the particle swarm-optimized support vector machine (PSO-SVM) is used for classifying and recognizing obstacles. On block data set B containing multi-view and multi-scale obstacle images, the recognition rate of this method can reach up to 86.2%, which shows the effectiveness of weighted fusion of global and local features. On data set A containing complete images of different distances, the detection success rate of long-distance obstacles can reach 80.2%. The validity of the proposed method based on structural constraints and feature fusion is verified.

Highlights

The study of cable inspection robots with long-time automatic operation has attracted great attention [1,2,3,4,5]
SVM1: Taking three types of samples as positive samples and the background as negative samples, the classifier is trained to identify whether the target is an obstacle or not; the classifier is trained to identify whether the target is an obstacle or not; SVM2: Taking the obstacle group as positive samples, the suspension clamp and damper as
SVM2: Taking the obstacle group as positive samples, the suspension clamp and damper as negative samples, the classifier is trained to determine whether the target is the obstacle group or not; negative samples, the classifier is trained to determine whether the target is the obstacle group or not; SVM3: Taking the suspension clamp as positive samples, and the damper as negative samples

Summary

Introduction

The study of cable inspection robots with long-time automatic operation has attracted great attention [1,2,3,4,5]. To achieve long-term operation, robots need to cross various types of obstacles. Real-time obstacle detection and recognition plays a crucial role in robot navigation and behavior planning. Chen Zhongwei [8] proposed an electromagnetic sensor navigation method for high-voltage line patrol robot. Three sets of electromagnetic sensor probes were designed on each robot arm to form a sensor probe array for identifying high-voltage conductor and obstacles. Three SVM classifiers are constructed as follows, and parameter C and gamma of each classifier is. Dataset B contains samples of different types, perspectives and scales. Each background image contains more image details, which are consistent with the region of interest detected by the bounding box algorithm. B: samples (a) obstacles group, (b)types, damper,. (c) suspension datasetofB dataset contains of different perspectives andclamp, scales.

Methods

Results

Conclusion