TITLE:
A Method for Train Rail Surface Condition Recognition Based on Local Inference Constrained Network
AUTHORS:
Jihong Zuo, Lili Liu, Yan Li, Chuanyin Yang
KEYWORDS:
Rail Surface Condition Recognition, Residual Spinal Network, Adversarial Network
JOURNAL NAME:
Journal of Computer and Communications,
Vol.13 No.4,
April
27,
2025
ABSTRACT: The condition of the rail surface plays a crucial role in the wheel-rail contact behavior. Accurately recognizing the rail surface condition can provide important support for the high-performance control of trains. However, the small amount of rail surface condition data leads to the lack of the feature space of the rail surface adhesion medium. Therefore, this paper proposes a method for recognizing the rail surface condition based on the local inference constrained network. Firstly, an adversarial network is used to generate diverse data samples while ensuring the semantic consistency of these samples. Meanwhile, an attention mechanism module is added to the feature extraction network to prominently highlight the information features of the target area and enhance the adaptability of the model. Secondly, a residual spinal network for local input decision-making is constructed, and the activation function is improved to accelerate the learning speed of the network and enhance the recognition accuracy. Finally, experiments are carried out to verify the effectiveness and feasibility of the proposed method.