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The pantograph–catenary system is crucial to the transmission of electrical power from catenary lines to electrified trains. The occurrence of arcs could damage or interrupt railway operations. We propose a CNN-based model to detect arcs and recognize their magnitudes. First, we decompose the pantograph videos recorded by a camera fixed on China High-Speed train into continuous frames, and grayscale-process and segment those images to obtain an arc image set. Then, we divide the image set into training samples and test samples. And the training samples are further divided into three classifications labeled as 0, 1, 2, which are used to train CNN model. The accuracy of CNN trained result reaches 0.95, and the loss function converges to 0.083. Second, we use the trained network to detect arcs in the images of the test samples, and convert arc detection results to a time series of arc scores. Thus the occurrence of arcs and their magnitudes can be determined. Finally, we conduct experiments to compare our approach with other models. The results demonstrate the approach's high accuracy, robustness, and high speed, when dealing with images taken from unstable surroundings. It could be applied to other EMU models or environments with adjusted parameters. [ABSTRACT FROM AUTHOR]