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Objects recognition from traffic video data using improved 2D convolutional stochastic configuration networks
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DOI：10.1016/j.array.2025.100377.png)
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Abstract
En 中文
With the fast development of advanced science and technology, the urban rail transit continues to develop rapidly, with the industry pays more attention to the operation safety and maintenance of trains. In this paper, an improved 2D convolutional stochastic configuration network (2DConSCN) based method is proposed to deal with traffic video for foreign object recognition. Comparing with the existing stochastic configuration networks,the proposed method retains the stochastic configured mechanism for the convolutional kernel weights. Moreover, a feature selection method is presented to improve the image representation ability. The proposed improved 2DConSCN method greatly reduces the number of parameters, and the trained model can quickly obtain results on test data. Experiments are performed on a rail transit dataset, the comparison results show that the proposed method gets better performance in the recognition task, showing its great potential to meet the requirement of railway monitoring.
Keywords:
Stochastic configuration networks
Convolutional kernel
Object recognition
Feature selection
Rail transit
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