An online arc detection approach using classification learning is proposed to solve the interference problem during locomotive running due to high false alarms in the real-time on-line monitoring of pantograph arc detection for electric locomotives. The approach firstly determines pantograph region through gradient projection. Then candidate arc regions are searched in the determined region
histograms of gradient image in these regions are extracted
and the classification learning approach is used to determine whether a frame is an arc or not. Finally
a multi-frame smoothing based approach is proposed to detect the pantograph arc clip. Experimental results show that the proposed multi-frame smoothing based approach improves the correct detection rate by about 8% and reduces the false alarm by about 32%.
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