A Multi-scale Detection Method for Dropper States in High-speed Railway Contact Network Based on RefineDet Network and Hough Transform
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摘要: 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。Abstract: 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%.
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Key words:
- Target detection /
- Deep learning /
- Contact network 4C /
- Defect analysis /
- Hough transform
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表 1 基于霍夫变换的吊悬线检测算法伪代码
Input: images from RefinDet Network (himage×wimage) Output: coordinate pairs at end points of line segmentsM(x1, y1),N(x2, y2) 1: Edge detection of images by Canny operator 2: Set θstep=1°, γstep=1 pixel; θmax=180°,
${\gamma _{\max } } = \sqrt { {{h} }_{ {\rm{image} } }^2 + {{w} }_{ {\rm{image} } }^2}$3: for i∈himage, j∈wimage do: 4: if edge[i][j]==255 then: 5: for m∈180°/θstep do: 6: γ=i·cos(θstep·m/180·pi)+j·sin(θstep·m/180·pi) 7: n =(γ+L)/γstep 8: count[n, m]+=1 9: store[n, m]=append((i, j)) 10: end for 11: end if 12: end for 
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表 2 ResNet18网络结构
层名称 输出 具体结构 conv1 P/2×Q/2 卷积核=7×7，步长=2 conv_2x P/4×Q/4 3×3最大池化，步长=2 2×残差映射模块1 conv_3x P/8×Q/8 2×残差映射模块2 conv_4x P/16×Q/16 2×残差映射模块3 conv_5x P/32×Q/32 2×残差映射模块4 1×1 全连接层
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表 6 不同网络模型在吊弦数据集上的定位结果
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表 7 3个实验测试集结果对比
实验 检测定位结果 灰度调整 霍夫变换 正常受力准确率(%) 非正常受力准确率(%) 1 ＋ — — 99.33 88.75 2 ＋ ＋ — 98.66 91.25 3 ＋ ＋ ＋ 99.33 97.50
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