目的 提高金属铸坯表面缺陷检测精度。 方法 由于金属铸坯表面上存在鱼鳞状构造，其亮度和背景区域纹理特征不一致，而且有缺陷和无缺陷的区域的灰度值极其相似，使得缺陷非常难以准确检测出来。为解决上述问题，以便更有效地检测表面缺陷，通过详细分析金属铸坯表面缺陷特征，将该类零件表面缺陷分为两种类型，提出一种基于优化 Gabor 滤波器的金属表面缺陷检测算法，该算法通过设计两种评价函数，利用评价函数最大限度地提高无缺陷和缺陷区域之间的能量差，以选取 Gabor滤波器四个最佳参数，同时使用双阈值滤波方法，以减少由于噪声和伪缺陷引起的测量误差。 结果 利用 3 种滤波算法对四十幅带有缺陷的图像进行试验，实验表明该算法在角部裂纹、细裂纹和伪裂纹检测精度分别达到 92.50%、 92.50%和 95.50%。 结论 Opt-Gabor 算法能根据已分类的两种不同类型的裂纹较为准确地检测出铸坏表面缺陷，在测量精度上略优于其他几种算法。

The work aims to improve the accuracy for detection of defects in surfaces of steel billets. Because of the presence of the scale-covered on the billet surface, its characteristics such as brightness and texture in the background region were inconsistent. Moreover, the similarities in the gray-levels of the defect and defect-free regions made it very difficult to accurately detect the defects. In order to solve the above-mentioned problems and to detect surface defects more effectively, a method (based on Opt-Gabor) for detection of defects in the surfaces of steel billets by analyzing the characteristics of the metal billet surface defects to classify such part surface defects into two types. In order to select the best four Gabor filter parameters, two evaluation functions were designed to maximize the use of the energy difference between defect-free and defect regions. Moreover, the dual-threshold filtering method was used to reduce the measurement errors caused by noise and pseudo defects. Forty images with defects were tested with three kinds of filtering algorithms. The experiment showed that the proposed method had a detection accuracy of 92.50%, 92.50% and 95.50% for corner cracks, thin cracks and pseudo crack respectively. Opt-Gabor algorithm can defect the billet surface defects in a more accurate way according to the two kinds of different cracks classified. With respect to measurement accuracy, Opt-Gabor algorithm is slightly superior to other algorithms.