Automatic Rail Component Detection Based on AttnConv-Net. (arXiv:2108.02423v2 [cs.CV] UPDATED)

The automatic detection of major rail components using railway images is
beneficial to ensure the rail transport safety. In this paper, we propose an
attention-powered deep convolutional network (AttnConv-net) to detect multiple
rail components including the rail, clips, and bolts. The proposed method
consists of a deep convolutional neural network (DCNN) as the backbone,
cascading attention blocks (CAB), and two feed forward networks (FFN). Two
types of positional embedding are applied to enrich information in latent
features extracted from the …

arxiv cv detection rail