In the intelligent traffic transportation, the security and stability are vital for the sustainable transportation and efficient logistics. The fault diagnosis on the catenary system is crucial for the railway transportation. For purpose of improving the detection capability for the faulted current-carrying ring and maintaining the efficiency of the railway system, a fault diagnosis method for the current-carrying ring based on an improved RetinaNet model with the spatial attention map and channel weight map is proposed. The local and global features are utilized respectively. The spatial attention maps are embedded into the original convolutional neural network to emphasize the interested local features and weaken the influence of other objects and background. The channel weight maps are introduced into the feature pyramid network of detection network to weight the multi-scale feature maps in channels. The representative global features are focused and unnecessary features are suppressed. The experimental results indicate that the proposed method has increased fault diagnosis accuracy for faulted current-carrying rings compared with the original detection network based on different backbones. It can be used in improving efficiency and safety of railway transport system.

在智能交通运输中，安全性和稳定性对于可持续交通和高效物流至关重要。悬链线系统的故障诊断对于铁路运输至关重要。为了提高故障载流环的检测能力并保持铁路系统的效率，提出了一种基于改进的RetinaNet模型并结合空间注意图和通道权重图的载流环故障诊断方法。 。分别利用局部和全局特征。空间注意力图被嵌入到原始的卷积神经网络中，以强调感兴趣的局部特征并削弱其他对象和背景的影响。将通道权重图引入检测网络的特征金字塔网络，对通道中的多尺度特征图进行加权。集中了代表性的全局特征，并抑制了不必要的特征。实验结果表明，与基于不同主干的原始检测网络相比，该方法对载流环的故障诊断具有更高的准确性。可用于提高铁路运输系统的效率和安全性。实验结果表明，与基于不同主干的原始检测网络相比，该方法对载流环的故障诊断具有更高的准确性。可用于提高铁路运输系统的效率和安全性。实验结果表明，与基于不同主干的原始检测网络相比，该方法对载流环的故障诊断具有更高的准确性。可用于提高铁路运输系统的效率和安全性。