多层次分割实现微小尺寸实时测量

Micro-dimension detection by means of multi-level segmentation

针对灰度图像多个斜坡状边缘目标的微小尺寸实时检测,提出了两次阈值分割的检测算法。预分割提取目标并确定其过渡区。而后分割是在过渡区搜索目标真实边缘,实现尺寸测量。实验表明,这种两次阈值分割算法对 0.5mm 以下目标测量优势明显,精度优于 0.001mm,并降低了噪声对真实边缘点数目的影响。在实时性能上,测量平均时间为 60ms,比拟合算法缩短了 40%,保证了检测系统下位机 2000 packages/h 的处理速率。

For the purpose of real-time micro-dimension detection of the multiple ramp-edged objects in a grey scale image, a twice-segmentation detection algorithm is proposed. The first segmentation is to extract object and determine transition area while the second segmentation is to search the real edges of an object in the transition area for detecting micro-dimension of the object. Experiments show that this twice threshold segmentation algorithm has obvious advantages for measuring objects smaller than 0.5 mm with better measuring accuracy than 0.001 mm and the influence of noise on real edge point number is reduced. The average measuring time for an object is 60 ms which is only 60% of that of fitting algorithm. This guarantees 2000 packages/h processing rate of the detection system.