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

作　　者：GU Guimei WEN Bokang JIA Yaohua ZHANG Cunjun 顾桂梅;温柏康;贾耀华;张存俊(兰州交通大学自动化与电气工程学院,甘肃兰州730070;中国铁路兰州局集团有限公司,甘肃兰州730030)

机构地区：[1]School of automation and electrical engineering,Lanzhou Jiaotong University,Lanzhou 730070,China [2]China Railway Lanzhou Group Co.,Ltd.,Lanzhou 730030,China

出　　处：《Journal of Measurement Science and Instrumentation》2024年第4期571-578,共8页测试科学与仪器（英文版）

基　　金：supported by Natural Science Foundation of Gansu Province(No.20JR10RA216)。

摘　　要：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。

关 键 词：catenary dropper fully convolutional one-stage(FCOS)network defect identification generalized intersection over union(GIoU) focal loss 

分 类 号：U226.8[交通运输工程—道路与铁道工程]