Detection Approach Based on an Improved Faster RCNN for Brace Sleeve Screws in High-Speed Railways

Abstract

Brace sleeve screws are an important component of catenary support devices in high-speed railways. According to the statistical data, there is a much risk for failure of brace sleeve screws during the operation of the catenary system. In order to obtain better results on the fault diagnosis for brace sleeve screws, it is of vital importance to develop a high-precision detection approach for brace sleeve screws. With the aim of solving the problems that the size of the brace sleeve screw is too small and the employed previous methods are difficult to detect accurately, a novel object detection network based on an improved Faster RCNN is proposed. Through the analysis of the factors that influence the detection accuracy for brace sleeve screws and the characteristics of original Faster RCNN, the concepts of discrimination maps and proposal maps are introduced for the improved Faster RCNN. In order to retain the characteristic information of brace sleeve screws, the high-resolution discrimination maps are adopted to identify the object type and the low-resolution proposal maps are adopted to generate the candidate boxes of region with an appropriate anchor scale configuration. Experimental results show that the proposed method has a higher detection accuracy for brace sleeve screws. In addition, compared with original Faster RCNN implementation based on the VGG-16 and ResNet-101 backbone, the detection accuracy of the improved Faster RCNN is increased by 8%. It also provides a new idea for general object detection of small targets.