Unified Deep Learning Architecture for the Detection of All Catenary Support Components
Wenqiang Liu, Zhigang Liu, Alfredo Núñez, Zhiwei Han
Abstract
With the rapid development of deep learning technologies, researchers have begun to utilize convolutional neural network (CNN)-based object detection methods to detect multiple catenary support components (CSCs). The literature has focused on the detection of specified large-scale CSCs. Additionally, CNN architectures have faced difficulties in identifying overlapping CSCs, especially small-scale components. In this paper, a unified CNN architecture is proposed for detecting all components at various scales of CSCs. First, a detection network for CSCs with large scales is proposed by optimizing and improving Faster R-CNN. Next, a cascade network for the detection of CSCs with small scales is proposed and is integrated into the detection network for CSCs with large scales to construct the unified network architecture. The experimental results demonstrate that the detection accuracy of the proposed CNN architecture can reach 92.8%; hence, it outperforms the popular CNN architectures.