Modulated deformable convolution based on graph convolution network for rail surface crack detection

Abstract

Accurate detection of rail surface cracks is essential but also tricky because of the noise, low contrast, and density inhomogeneity. In this paper, to deal with the complex situations in rail surface crack detection, we propose modulated deformable convolution based on a graph convolution network named MDCGCN. The MDCGCN is a novel convolution that calculates the offsets and modulation scalars of the modulated deformable convolution by conducting the graph convolution network on a feature map. The MDCGCN improves the performance of different networks in rail surface crack detection, harming the inference speed slightly. Finally, we demonstrate our methods’ numerical accuracy, computational efficiency, and effectiveness on the public segmentation dataset RSDD and our self-built detection dataset SEU-RSCD and explore an appropriate network structure in the baseline network UNet with the MDCGCN.