基于集成学习的铸件缺陷识别方法

针对铸件图像噪声多和对比度不足引起的缺陷识别困难的问题，文中提出了一种基于集成学习的铸件缺陷识别方法。首先，该方法采用灰度变换法、双边滤波以及自适应图像分割法对铸件图像进行预处理。然后，通过提取方向梯度直方图（Histogram of Oriented Gradients, HOG）特征、不变矩特征和局部二值模式（Local Binary Pattern, LBP）纹理特征构建全信息特征集，并结合支持向量机递归特征消除（Support Vector Machine-Recursive Feature Elimination, SVM-RFE）算法筛选铸件缺陷敏感特征。最后，利用Adaboost-RF（Adaptive Boosting-Random Forest）方法构建铸件缺陷识别模型。对比实验结果表明，该模型不仅可以有效提取缺陷敏感特征，而且相较于其他分类器具有更好的分类性能和泛化能力。

Casting Defect Recognition Method Based on Ensemble Learning

Aiming at the difficulty in identifying defects caused by excessive noise and insufficient contrast in casting images, a method for identifying defects in castings based on ensemble learning is proposed in this paper. At first, grayscale transformation, bilateral filtering and adaptive image segmentation are employed to preprocess the casting image. Then, the HOG (Histogram of Oriented Gradients) feature, moment invariant feature, and LBP (Local Binary Pattern) texture feature are extracted to construct the full-information feature set. Meanwhile, the SVM-RFE (Support Vector Machine-Recursive Feature Elimination) algorithm is utilized to select sensitive features. In the end, the Adaboost-RF (Adaptive Boosting-Random Forest) method is used to recognize the casting defect. The results of the comparison experiment show that this method can effectively extract the sensitive features from the full-information feature set. Moreover, it has better classification performance and generalization ability than other classifiers.