Adaptive Detection Algorithm for High-Speed Railway Fasteners by Vision
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摘要: 为了实现高铁缺陷扣件的准确、快速和自动化检测，提出一种基于图像处理技术的高铁扣件自适应视觉检测算法. 针对高铁扣件图像的特性，使用改进的LBP (local binary pattern)算子提取扣件的显著特征；在扣件特征图的基础上，采用模板匹配算法得到扣件区域在原始图中的精确位置，进而得到扣件子图并用扣件的位置信息校验定位结果；以相邻两个扣件子图的差值作为判断依据，如果差值大于预设的阈值，相应的扣件则被判断为缺陷扣件. 将该检测算法应用于高铁工务部门提供的真实扣件图. 研究结果表明：本文提出的自适应扣件检测算法在雨天的表现最差，检出率为96%，误检率为0.50%；在晴天的表现最好，检出率为100%，误检率为0.22%；在不同天气、光照、环境下的综合检出率为99%，综合误检率为0.33%.Abstract: In order to achieve accurate, rapid and automatic detection of high-speed railway defective fasteners, an adaptive vision detection algorithm for high-speed railway fastener was proposed based on image processing technology. Aiming at the particularity of high-speed railway fastener images, the improved LBP (local binary pattern) operator was used to extract the salient features of fastener. Based on the prominent feature maps of fastener, the template matching algorithm was used to obtain the precise position of fastener region in the original image, and then get the sub-map of fastener and use the position information of fastener to verify the localization result; The difference between two adjacent sub-maps was used as the judgment basis, if the difference was greater than the preset threshold, the corresponding fasteners were judged as defective fasteners. The detection algorithm was applied to the real fastener image provided by the track maintenance division. The results show that the adaptive fastener detection algorithm proposed in this paper performs worst on rainy days, with a correct detection rate of 96% and a false detection rate of 0.50%; It performs best on sunny days, with a correct detection rate of 100% and a false detection rate of 0.22%; It achieves a comprehensive correct detection rate of 99% and a comprehensive false detection rate of 0.33% under different weather, lighting and environment.
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Key words:
- high-speed railway /
- fastener detection /
- machine vision /
- image processing /
- pattern recognition
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图 1 原始LBP的邻域结构及其权重
Figure 1. Neighborhood structures of original LBP and its weights (N = 8，R = 1)
图 2 改进LBP的邻域结构及其权重
Figure 2. Neighborhood structures of improved LBP and its weights (N = 4，R = 1)
图 3 使用不同邻域半径的扣件改进LBP
Figure 3. Fastener image coded by improved LBP with different neighborhood radius
图 4 原始LBP与改进LBP对扣件噪声图像的编码结果对比
Figure 4. Comparison of coding results for fastener noise images using original LBP and improved LBP
表 2 扣件识别结果（Td = 0.3）
Table 2. Results of fastener recognition algorithm (Td = 0.3)
图像类别 输入图像总数/（× 103 幅） 正常图像数/幅 缺陷图像数/幅 正确检出/幅 错误检出/幅 检出率/% 误检率/% 高光照 5 4 968 32 32 16 100.00 0.32 低光照 5 4 964 36 35 21 97.22 0.42 雨天 5 4 975 25 24 25 96.00 0.50 晴天 5 4 966 34 34 11 100.00 0.22 新线路 5 4 972 28 28 12 100.00 0.24 老线路 5 4 955 45 45 14 100.00 0.28 综合 30 29 800 200 198 99 99.00 0.33 
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