Approach on Recognizing Uncovered Earth Region from Aerial Images Based on Multi-feature Fusion

Abstract

It is one of the main hidden dangers of power transmission lines accidents if there is the uncovered earth under or near power transmission lines. It can give the important early warning message to prevent the accidents through recognizing the uncovered earth region from aerial images of Unmanned Aerial Vehicle (UAV) power transmission lines inspection. Due to the low recognizing precision of Mask RCNN CNN (Mask Convolution Neural Network), this paper proposed an approach to recognize the uncovered earth region from aerial images of UAV by image feature fusion. The HOG and LBP features of aerial images were extracted and their dimension were reduced. Then these two features were fused by different weights. The experiments show that, (1) the average precision of recognizing the uncovered earth region can be above 80%, which is the lowest requirement to use; (2) the weights of two features should make the orders of magnitude of the two features as equal as possible. The approach is application for the first image filtering by the UAV airborne platform because it not only has enough good recognizing precision but also is very rapid, which provides a novel way for objective recognition from UAV aerial images.