中文摘要：

连铸热坯表面缺陷机器视觉检测系统中,获得清晰稳定信噪比高的图像是检测能否成功的前提条件。针对当前连铸坯表面缺陷获取图像质量及清晰度问题,提出一种基于聚焦平方梯度及CCD靶面照度参数的缺陷图像质量控制算法。选取连铸热坯表面上的一个标志点作为对象,首先采用聚焦窗口平方梯度函数获得系列离焦平面的清晰度最高图像对象,再通过识别该对象和分析对象的面积损失率获得CCD靶面照度参数,进而获得全局采集图像的质量最清晰点。该算法解决了连铸热坯表面缺陷成像系统中焦平面选择及CCD积分时间控制,具有很好的实用性。同时,该算法也为其他机器视觉工程的图像采集提供良好的指导作用。

英文摘要：

In the hot continuous casting billet surface defect inspection system based on machine vision, acquiring a high signal-to-noise image is the key for successful inspection. To solve the disadvantages existing in current machine vision engineering, a new algorithm with improved image definition is presented based on both focus window and CCD target area illumination parameters. It selects a target object from series of hot continuous casting billet surface images, and then acquires the optimum articulation through focus window square gradient algorithm. By recognizing and calculating target’s area loss rate, target area parameter evaluation can be done. The global optimum image quality point is achieved. The algorithm is effective in selecting focus plane and shutter time during hot continuous casting surface imaging process and is of a good practical value. At the same time, the algorithm is useful for image collecting work in other machine vision engineering.