A two-stage classification method for borehole-wall images with support vector machine

Abstract

Analyzing geological drilling hole images acquired by Axial View Panoramic Borehole Televiewer (APBT) is a key step to explore the geological structure in a geological exploration. Conventionally, the borehole images are examined by technicians, which is inefficient and subjective. In this paper, three dominant types of borehole-wall images on coal-rock mass structure, namely, border images, fracture images and intact rock mass images are mainly studied. The traditional image classification methods based on unified feature extraction algorithm and single classifier is not effect for the borehole images. Therefore, this paper proposes a novel two-stage classification approach to improve the classification performance of borehole images. In the first-stage classification, the border images are identified from three kinds of images based on texture features and gray-scale histograms features. For the remaining two types of images, in the second-stage classification, Gabor filter is first applied to segment the region of interest (ROI) (such as microfracture, absciss layer and horizontal cracks, etc.) and the central interference region. Then, using the same feature vector after eliminating the central interference region, fracture images are separated from intact rock mass images. We test our two-stage classification system with real borehole images. The results of experimental show that the two-stage classification method can effectively classify three major borehole-wall images with the correction rate of 95.55% in the first stage and 95% in the second stage.

Robust Face Mask Detection by a Socially Assistive Robot Using Deep Learning

“…Each classifier consists of an extended ResNet-50 two-stage CNN. We use two-stage CNNs herein as they have been shown to achieve higher detection and classification accuracy compared to single-stage CNNs [63], as well as being able to incorporate robust feature extraction when subtle variations exist [64].…”

“…compared to single-stage CNNs [63], as well as being able to incorporate robust feature extraction when subtle variations exist [64].…”