Catenary dropper fault identification based on improved FCOS algorithm

基于改进FCOS算法的接触网吊弦故障识别

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 parameterαin 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 0.9512.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.

接触网吊弦工作环境恶劣,在列车行驶过程中还会遭受受电弓的冲击作用,可能出现吊弦线松弛、断裂和载流环断裂等故障。由于吊弦的故障检测网络智能性低、准确率差,本文提出一种改进的全卷积一阶段目标检测(Fully convolutional one-stage object detection,FCOS)网络来提高对吊弦状态的检测能力。首先,通过调节网络焦点损失函数中的α参数消除网络训练过程中正负样本不平衡问题。其次,引入广义交并比(Generalized intersection over union,GIoU)计算,增强网络在回归计算时对预测框和目标框相对空间位置的识别能力。最终,使用改进后的网络对吊弦图片进行状态检测,检测速度为150张每毫秒,对不同状态检测的MAP为0.9512。通过与其他目标检测网络的仿真对比,证明了改进后的FCOS网络在检测时间和精度上同时具有优势,能准确地对吊弦状态进行识别。