基于旋转不变HOG特征的焊缝缺陷类型识别算法

根据某钢管厂实际采集到的X射线焊缝图像，并通过对焊缝缺陷多样性和形态多变性特点的研究，给出一种基于旋转不变HOG特征提取的焊缝缺陷类型识别算法. 首先，将项目前期已经检测到的多种缺陷进行分类和统计，截取每幅焊缝图像的ROI部分，构成实验所需的缺陷样本. 通过尺度变换和圆形细胞划分方式，得到具有尺度不变性和旋转不变性的HOG特征，将所有样本特征进行PCA降维，维数由贡献度决定. 最后使用LSSVM模型对缺陷进行类型识别. 通过研究block块重叠范围对识别正确率的影响，发现在一定范围内，重叠范围越大，识别正确率越高. 该算法通过改进传统HOG特征提取方式，提高了缺陷识别的正确率.

Identification of Weld Defects Based on Rotation-Invariant HOG Feature

According to the X-ray weld image collected by a steel pipe factory and the study on diversity and morphological variability of weld defects, a weld defect identification algorithm based on rotation invariant HOG feature extraction is proposed. First of all, we classify different types of defects detected to extract ROI of each image, all of which constitute the defect samples required by the experiment. By means of scale transformation and circular cell division, we obtain HOG characteristics with scale invariance and rotation invariance. Then all the sample features are reduced by PCA dimensionality reduction. The dimension is determined by the contribution. Finally, the LSSVM model is used to identify the defects. By studying the effect of block overlap on the recognition accuracy rate, it is found that the higher overlap range, the higher correctness in a certain unit. The algorithm improves the accuracy of defect recognition by improving the traditional HOG feature extraction method.