Railway Fastener Detection Using Gaussian Mixture Part Model
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摘要: 针对采集图像中铁路扣件存在形状的变化、扣件图像的光照差异较大和扣件被异物局部遮挡的问题，根据对可变形部件模型算法和高斯混合模型的研究，提出了高斯混合部件模型算法. 结合扣件图像边缘特性及改进的Roberts算子计算图像梯度，将归一化后的方向梯度直方图特征作为高斯混合部件模型算法的底层特征，根据扣件形状划分部件，部件之间的相对位置采用星型连接方式度量，运用余弦相似性度量部件中方向梯度直方图特征的相似度，部件模型使用高斯混合模型并采用期望最大化算法迭代求解. 将高斯混合部件模型算法应用于扣件检测中，最终平均检测效果为漏检率3.16%、误检率9.80%、正确率90.27%.Abstract: Herein, the shape deformations in the collected images of railway fasteners, the large illumination difference in the fastener image, and the partial occlusion of the fastener by foreign objects is addressed. A Gaussian mixture part model (GMPM) algorithm is proposed to address these issues. This model is based on previous research of deformable part model; however, it was further refined by combining with a Gaussian mixture model (GMM) algorithm. The image gradient was calculated by combining the edge characteristics of the fastener image and application of the improved Roberts operator to the image. Normalized histogram of oriented gradient (HOG) features were used as the basis of the GMPM algorithm. The image was divided into a number of regions considering the shape of the fastener, and a star connection was used to measure the relative positions of the various subsections of the subdivided image. Cosine similarities were computed to measure the similarity of the HOG features of the different parts of the image. The part model was solved iteratively by using the GMM with an expectation-maximization algorithm. By using the GMPM algorithm to detect defects in railway fasteners, an average accuracy of 90.27% for detection rate is achieved, while the average missed detection rate, and the average false detection rate are 3.16% and 9.80%, respectively.
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Key words:
- GMM /
- part model /
- HOG feature /
- cosine similarity /
- fastener detection
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表 1 高斯混合部件模型扣件检测结果
Table 1. Fastener detection results of the gaussian mixture part model
方法 样本类别 样本总数 正样本数 负样本数 样本检测结果 漏检率 误检率 正确率 真正 假正 假负 真负 本文方法 高亮度 2 704 2 676 28 2 418 0 258 28 0 0.096 0.905 中亮度 3 725 3 681 44 3 279 2 402 42 0.045 0.109 0.892 低亮度 3 085 3 062 23 2 799 1 263 22 0.043 0.086 0.914 文献[4] Jitong 4 204 4 185 19 3 425 0 760 19 0 0.182 0.819 Yiwan 3 738 3 651 87 3 012 8 639 79 0.092 0.175 0.827 文献[3] – 3 391 3 370 21 2 647 1 723 20 0.048 0.215 0.786 文献[2] – 1 500 1 100 400 – – – – – – 0.976 注：“–”表示原文献中未给出相应数据. 
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