为了实现对大型厚壁压力容器焊缝缺陷的准确识别，提高缺陷评定的准确性和检测效率，在基于标记的改进分水岭TOFD检测图像分割的基础上，结合典型缺陷图像的纹理特征，从图像空间域和频域特征，分别利用局部相位量化和局部二值模式获取缺陷区域的局部邻域特征参数，将二者特征参数进行归一化融合，再将融合特征向量用支持向量机进行分类识别。试验结果表明，检测图像4×4分块后提取的熔合特征识别率最优，分类识别正确率达到87.10%。

In order to realize the accurate identification of weld defects in large-scale thick-walled pressure vessels and to improve the accuracy of defect evaluation and detection efficiency, based on the mark-based improved watershed time flight of diffraction (TOFD) image segmentation and combined with the texture features of typical defect images, local phase quantization and local binary patterns are used respectively from the image spatial domain and frequency domain characteristics. The localized two value model can provide the local neighborhood characteristic parameters of the defect region, and through the normalization and fusion of the two feature parameters, the fusion feature vector is then classified by the support vector machine. The experimental results show that the fusion feature recognition rate proposed after detecting the 4 and 4 block of the image is the best, and the classification recognition accuracy rate reaches 87.1%.