一种基于RefineDet网络和霍夫变换的高速铁路接触网吊弦状态多尺度检测方法

A Multi-scale Detection Method for Dropper States in High-speed Railway Contact Network Based on RefineDet Network and Hough Transform

针对高速铁路接触网吊弦的状态检测问题,该文提出一种基于RefineDet网络和霍夫变换的吊弦多尺度定位与识别方法。通过设计RefineDet网络的粗调和精调模块对吊弦整体结构进行定位,采用霍夫变换锁定吊弦中部吊悬线所在直线,并利用旋转因子沿直线方向提取吊悬线区域;以吊悬线区域代替吊弦结构整体区域送入分类网络进行训练,通过所建立的多尺度吊弦状态检测模型,实现吊弦状态的精确识别。实验结果表明,吊弦定位模型的准确率达95.3%以上;霍夫变换可排除无效区域对吊弦状态识别的干扰,提高分类网络的训练速度,吊弦状态识别模型准确率达97.5%以上。

In order to solve the problems of detection and state analysis of high-speed railway catenary droppers,this paper proposes a multi-scale detection method for dropper states based on Refinedet network and Hough transform.First,the positioning result of droppers through Refinedet network is obtained,and Hough transform is used to locate where the dropper line is;Then the surrounding area of the dropper line is extracted with a ralated twiddle factor.Those extracted areas,replacing the results of detection net,are fed into classification network for training the final dropper state analysis mode.Experiments show that the accuracy of dropper detection model is over 95.3%,and the dropper state analysis model can eliminate the impact of meaningless area pixels while accelerating training process,the final state analysis model achieves a high accuracy over 97.5%.