Transmission Line Obstacle Detection Based on Structural Constraint and Feature Fusion

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.