分析了光斑图像成像特点和理想光斑灰度分布模型, 针对含有多个不同尺度光斑的图像, 提出了一种可以在复杂环境下一次性快速检测出多个光斑中心的方法。该方法基于高斯模糊后光斑中心不变的性质, 先对含有大量光斑的图像进行快速多级高斯模糊, 构建其高斯尺度空间; 然后, 使用加速的非极大值抑制方法在尺度空间内寻找多个尺度的局部极值, 初步确定各光斑中心的像素级坐标; 最后, 联合这些坐标的邻域像素, 拟合局部曲面, 得到光斑中心的亚像素级精确位置。利用仿真实验和实物实验验证了提出方法的有效性。结果表明: 该算法对640 pixel×480 pixel图像, 处理时间仅需50ms, 每千个光斑的平均检测时间为23 ms, 在复杂环境下正确率可达89%。此外, 该方法对弱光斑较敏感, 适合快速处理含有大量不同尺度光斑的图像, 并能够有效减少光斑的错检和漏检。由于检测速度快, 自适应性强, 在实际应用中取得了良好的检测效果。

The imaging characteristics of an image with light spots and the grey-level distribution model of an ideal light spot were analyzed. A fast and accurate algorithm to detect simultaneously multiple light spot centers in a complex imaging environment was proposed for the image with multi-scale light spots. As the centers of light spots would not be changed after blurring the spot image with Gaussian kernels, the image with massive multi-scale light spots was blurred firstly with multilevel Gaussian kernels to fast establish a Gaussian scale-space of the spot image. Then an efficient non-maximum suppression algorithm was applied to find local extremums in multiple scales and to determine the pixel level coordinates of the light spot centers in the scale-space preliminarily. Finally, combined with the neighboring pixels of these pixel level coordinates, sub-pixel accurate locations of the spot centers were obtained by local surface fitting. The validity of proposed algorithm was verified by simulation and experiments. The results for an image of 640 pixel ×480 pixel show that the processing time is 50 ms, average detection time for per thousand light spots is only 23 ms and the detection accuracy is 89% in many complex situations. Moreover, the algorithm is sensitive to low-light spots and can process the images with different scale spots in low contrast scenes, usually offering a low error rate and miss rate. Due to the high detection speed and good stabilitiy, the proposed algorithm performs well in real vision measurement systems.