Research on fault identification method of railroad fastener based on center point prediction network

Currently, image detection based on deep learning has a good application prospect for railroad fault diagnosis. However, the existing methods of applying deep learning to fastener fault detection more or less have some drawbacks, such as computational complexity, slow detection speed, low detection accuracy, poor robustness, etc. To address these issues, this paper introduces a fastener fault identification method for railroad lines based on CenterNet. By introducing the Convolutional Block Attention Module and the jump connection mechanism, and conducting comparative experiments on different networks, it is proved that the network proposed in this paper has a better speed and accuracy for fastener detection.