Abstract: Measuring the motion characteristics of circuit breaker moving contacts can help diagnose the operating status of the circuit breaker. Currently, most measurement methods are "contact" testing methods, which generally have problems with inconvenient installation and low measurement accuracy. Therefore, a new model that can achieve non-contact measurement method is proposed. Firstly, the multi-scale feature fusion structure is used to fuse the extracted multi-layer depth features. Secondly, the improved Transformer structure with introduced convolution operation is used for feature enhancement. Finally, the prediction head is used to predict the tracking results. Experimental analysis shows that compared with the original algorithm, the tracking success rate of the tracking algorithm has increased by 2.6%, and the precision has increased by 13.9%. The model can achieve accurate tracking and obtain the circuit breaker stroke time curve, which can reasonably reflect the action char-acteristics of the circuit breaker operating mechanism. 

Key words: circuit breaker, target tracking, Transformer, multi-scale feature fusion, convolutional neural network