Abstract
In this work, the authors design an object detection method by the characteristics of the station with convolutional neural network, such as small scale-to-height ratio change and relatively fixed position. In order to realise the pre-processing and feature extraction of the station data, they collect the video samples and filter, frame, label and process to these samples. Also then, the training sample data and the test sample data are divided proportionally to train the station detection model. After that, the sample is tested to evaluate the effect of the training model in practice. The simulation experiments proved its validity.
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