针对重轨图像两个边缘像素特征不一致, 传统边缘算子检测法难以精确提取边缘的问题, 提出了一种新的边缘提取方法。该方法利用灰度强对比度拉伸算法对重轨表面和背景进行差异化拉伸, 增强边缘信息, 削弱背景信息。运用最大方差比算法选取增强后图像的最佳阈值实现二值化。最后, 运用递归连通域标识法定位边缘像素坐标, 完成图像分割。对随机选取的30幅图像进行分析表明：处理后的图像边缘灰度特征明显增强, 有效地抑制了表面纹理及虚假边缘。重轨表面像素宽度波动减少到-0.64%~0.34%。离散预处理算法通过遍历寄存器全局数组, 减少分割时间至10.165 s。该方法在抗干扰性、准确性及时效性等方面优于传统边缘算子检测法, 适用于在线工业检测系统。

As the two edge pixels of a heavy rail image is not identical, the classical edge operators are difficult to achieve the edge extraction and segmentation. Therefore, this paper proposed a new algorithm to enhance and extract images. A strong contrast stretching algorithm was used to stretch the rail surface and the background differently, enhance the edge information and weaken the background information. Then, the maximum variance method was taken to select the optimal threshold to implement the binarilization. Finally, the recursion connected domain marker algorithm was used to locate the pixel coordinates of edge to achieve the image segmentation. 30 images were chosen to a discretion experiment, and results indicate that the gray features of image edge are enhanced clearly, surface textures and false edges are restrained availably. Moreover, the pixel width fluctuating range is reduced from -0.64% to 0.34%. With the discrete pretreatment algorithm via addressing global array of a register, the segmentation time has been decreased to 10.165 s. The algorithm is better than the classical edge operators in the precision, correctness and the timeliness and is more suitable for on-line detection systems.