针对现有钢轨焊缝错边量测量方法在不同光照环境下适应能力不强、定位精度不高、稳定性不好的问题，提出了基于水平集的钢轨焊缝区域定位方法。首先，采用水平集对预处理后的焊缝图像进行轮廓分割，将焊缝与轨腰、轨头和背景进行分离；其次，采用面积排序和连通域相结合的方法排除轮廓干扰，实现焊缝轮廓的粗定位；再次，采用两次排序法实现焊缝轮廓的精定位；最后，对焊缝轮廓中横坐标采用排序法实现钢轨焊缝区域的自动定位。通过模拟不同的光照环境，对60 kg/m钢轨焊缝区域进行定位实验，验证了该方法具有适应能力强、精度高和稳定性好的优点，可用于焊轨基地焊缝错边量的自动检测。
An orientation method for rail weld region based on level set is proposed to improve the adaptability, stability and accuracy of weld positioning under different illumination conditions. Firstly, in order to separate welds from rail waists, rail heads and background, level set is used to segment the contours in preprocessed weld image. Secondly, area sorting and domain connecting are used in combination to eliminate contour interference and achieve coarse positioning of weld contour. The weld contour is then accurately positioned by using double sorting method. Finally, the rail weld region is automatically positioned by sorting the abscissa of weld contour. Positioning experiments for the weld region of 60kg/m rail are conducted under different illumination conditions. Experiments demonstrate the advantages of strong adaptability, high accuracy and good stability, and prove that the proposed method can be used for automatically detecting the weld misalignment in welded rail site.
[1] 张鹏贤, 韦志成, 刘志辉. 管道焊口间隙量与错边量的激光视觉检测[J]. 焊接学报, 2018, 39(11):103-107. Zhang P X, Wei Z C, Liu Z H. Laser visual measurement for gap values and misalignment values of pipeline welding groove[J]. Transactions of the China Welding Institution, 2018, 39(11):103-107. (in Chinese)
[2] 陈根余, 蔡兴, 谭力鹏, 等. 错边量对车用镀锌钢光纤激光焊接性能的影响[J]. 激光技术, 2012, 36(5):577-581. Chen G Y, Cai X, Tan L P, et al. Effect of align deviation value on fiber laser welding property of automotive galvanized steel[J]. Laser Technology, 2012, 36(5):577-581. (in Chinese)
[3] Xu K, Zhou P, Hu C. 3-D detection technique of surface defects for heavy rail based on binocular stereo vision[C]//International Symposium on Advanced Optical Manufacturing and Testing Technologies:Optical Test and Measurement Technology and Equipment. International Society for Optics and Photonics, 2012.
[4] 赵洁. 基于多传感器信息融合的轨道缺陷在线检测方法的研究[D]. 成都:西南交通大学, 2015.
[5] Zhao Y, Sun J H, Ma J, et al. Application of the hybrid laser ultrasonic method in rail inspection[J]. Insight:Non-Destructive Testing and Condition Monitoring, 2014, 56(7):360-366.
[6] 周文果, 熊邦书, 莫燕, 等. 钢轨焊缝错边的视觉检测方法[J]. 半导体光电, 2018, 39(2):264-267. Zhou W G, Xiong B S, Mo Y, et al. Visual inspection method for misalignment of rail welding[J]. Semiconductor Optoelectronics, 2018, 39(2):264-267. (in Chinese)
[7] 刘松, 熊邦书. 基于计算机视觉的钢轨焊缝自动定位方法[J]. 应用科学学报, 2019, 37(6):844-850. Liu S, Xiong B S. Automatic orientation method for rail weld based on computer vision[J]. Journal of Applied Sciences, 2019, 37(6):844-850. (in Chinese)
[8] Zhang H, Jin X, Wu Q M J, et al. Automatic visual detection system of railway surface defects with curvature filter and improved Gaussian mixture model[J]. IEEE Transactions on Instrumentation and Measurement, 2018, 67(7):1593-1608.
[9] Yu H, Li Q, Tan Y, et al. A coarse-to-fine model for rail surface defect detection[J]. IEEE Transactions on Instrumentation and Measurement, 2019, 68(3):656-666.
[10] 占栋, 于龙, 肖建, 等. 钢轨轮廓全断面高精度动态视觉测量方法研究[J]. 铁道学报, 2015, 37(9):96-106. Zhan D, Yu L, Xiao J, et al. Study on high-accuracy vision measurement approach for dynamic inspection of full cross-sectional rail profile[J]. Journal of the China Railway Society, 2015, 37(9):96-106. (in Chinese)
[11] Feng H, Jiang Z, Xie F, et al. Automatic fastener classification and defect detection in visionbased railway inspection systems[J]. IEEE Transactions on Instrumentation and Measurement, 2014, 63(4):877-888.
[12] 王延哲, 陈志强, 王雅婷. 基于机器视觉技术的轮轨横向位移测量方法[J]. 铁道建筑, 2018, 58(11):135-138. Wang Y Z, Chen Z Q, Wang Y T. Measurement method of lateral displacement between wheel and rail based on machine vision technique[J]. Railway Engineering, 2018, 58(11):135-138. (in Chinese)
[13] Cheng D, Shi D, Tian F, et al. A level set method for image segmentation based on Bregman divergence and multi-scale local binary fitting[J]. Multimedia Tools and Applications, 2019, 78(15):20585-20608.
[14] Lü T, Yang G, Zhang Y, et al. Vessel segmentation using centerline constrained level set method[J]. Multimedia Tools and Applications, 2019, 78(12):17051-17075.
[15] 王彬, 王国宇. 基于广义Gamma分布的高分辨率SAR图像海岸线检测[J]. 电子学报, 2018, 46(4):827-833. Wang B, Wang G Y. A coastline detection method in high-resolution SAR images based on the generalized Gamma distribution[J]. Acta Electronica Sinica, 2018, 46(4):827-833. (in Chinese)
[16] Wu X, Zhao J, Wang H. Face segmentation based on level set and improved DBM prior shape[J]. Progress in Artificial Intelligence, 2019, 8(2):167-179.
[17] 张庆春, 佟国峰, 李勇, 等. 基于多特征融合和软投票的遥感图像河流检测[J]. 光学学报, 2018, 38(6):320-326. Zhang Q C, Tong G F, Li Y, et al. River detection in remote sensing image based on multi-feature fusion and soft voting[J]. Acta Optica Sinica, 2018, 38(6):320-326. (in Chinese)
