对基于嵌入式平台的视觉测量系统进行了改进,将数字信号处理(DSP)中基于双阈值的搜索算法移植至现场可编程门阵列(FPGA),解决了传统的全局搜索与邻域搜索中目标提取准确性与数据更新率之间的矛盾。在嵌入式平台的FPGA中实现了合作目标标记点的像素级算法。在相机图像的传输过程中完成了基于双阈值全局搜索的标记点像素检测、像素区域识别、10级流水运算的质心提取、多标记点并行运算等功能,避免了邻域搜索带来的邻域尺寸设定、系统更新率、合作目标速度之间的矛盾。实验结果表明,应用文中方法后,视觉测量系统在2048×2048分辨率、5标记点的情况下的数据更新率达到186.33 frame/s;与改进前的全局搜索和邻域搜索相比,分别提高了33.5倍和2.7倍,使视觉测量系统的应用范围扩展到了高速运动目标测量领域。

The vision measurement system based on a embedded platform was improved. A research algorithm based on dual-threshold in Digital Signal Processing(DSP)was transplant in a Field Programming Gata Arrage(FPGA)to resolve the conflict between target extraction accuracy and data update rate of traditional global search and adjacent domain search. A pixel-level algorithm for cooperative targets was implemented in FPGA on embedded platform. Some functions based on dual-threshold searching in frame data transmission, such as pixel detection, pixel region identification, centroid calculation by a 10-level pipeline, and parallel computing in a multiple marker region were realized. Benefited from this, the contradiction among the neighborhood-size setting, system update rate and the target speed was avoided. Experimental results show that the data update rate of the vision measurement system with proposed method is up to 186.33 frame/s base on 5 markers with the resolution of 2048×2048. Comparing with the time-consuming of global search and neighborhood search without the proposed structure, the speed is increased by 33.5 times and 2.7 times, which extends the application of the system to the measurement of high-speed moving targets.