Catenary dropper fault identification based on improved FCOS algorithm

The contact network dropper works in a harsh environment, and suffers from the impact effect of pantographs during running of trains, which may lead to faults such as slack and broken of the dropper wire and broken of the current-carrying ring. Due to the low intelligence and poor accuracy of the dropper fault detection network, an improved fully convolutional one-stage (FCOS) object detection network was proposed to improve the detection capability of the dropper condition. Firstly, by adjusting the parameter α in the network focus loss function, the problem of positive and negative sample imbalance in the network training process was eliminated. Secondly, the generalized intersection over union (GIoU) calculation was introduced to enhance the network’s ability to recognize the relative spatial positions of the prediction box and the bounding box during the regression calculation. Finally, the improved network was used to detect the status of dropper pictures. The detection speed was 150 sheets per millisecond, and the MAP of different status detection was 0.951 2. Through the simulation comparison with other object detection networks, it was proved that the improved FCOS network had advantages in both detection time and accuracy, and could identify the state of dropper accurately.