受扫查图像效果和检测人员能力与主观因素的影响，检测人员在使用超声衍射时差法（TOFD）的D扫描图像判断焊缝缺陷类型时，存在可靠性低、争议大、速度慢的问题。为了提高缺陷类型识别的准确性及效率，分析了焊缝缺陷的TOFD-D扫描图像特征，构建了一种基于区域的卷积神经网络（Faster RCNN），对焊缝缺陷D扫描图像中的缺陷类型进行自动识别；在网络训练过程中，提出了采用样本扩展及建议框优化方法以提高网络训练的稳定性及效率。结果表明：缺陷的TOFD-D扫描图像的轮廓与缺陷几何形状密切相关，可用于判断缺陷类型；Faster RCNN网络虽然可能对界面波及噪声造成误判，但对缺陷类型的识别率可达到97%以上，可实现缺陷类型的自动识别，并具有高识别率、鲁棒性和抗干扰能力。

Manual recognition of the type of weld defects through ultrasonic Time of Flight Diffraction (TOFD) image has disadvantages of low efficiency and reliability, due to the limitation of experience and knowledge of the tester. In order to improve the accuracy and efficiency of defect's identification, characteristics of weld defect of TOFD-D scan imagery were analyzed, and Faster Region based Convolutional Neural Networks(Faster RCNN) was proposed to auto-recognize the defect's type. Besides, the methods of proposed box optimization and sample expanding were also explored to improve training efficiency and stability. Finally, recognition effect and influences were analyzed. Research results show that D-scan image of the weld defect is closely related to the defect's geometry which can be used to distinguish the defect's type. Although the Faster RCNN network may misjudge noise to the interface, auto-recognition of the defect's type can be implemented through Faster RCNN with high recognition rate, robustness and anti-jamming ability, in which recognition accuracy of more than 97% is achieved.