AbstractDefect recognition and assessment are essential technologies for the safety of pipelines. Recently, deep neural network methods have shown great progress in recognizing pipeline weld defects. However, the current methods cannot achieve online quality assessment for overall girth welds. To solve this problem, this paper proposes a novel quality assessment algorithm for defect recognition and assessment of X-ray girth weld images. First, a two-stage defect classifier based on residual neural network (ResNet) is proposed, which consists of a ResNet18 region classifier and a ResNet50 defect classifier. It can make up for the lack of a single classifier so that the performance and speed of defect recognition can be improved. Second, the feature-extraction calculator is used to extract and calculate the area of the weld defects based on the smallest bounding rectangle method. Third, we propose a quality evaluator based on an improved ensemble learning algorithm and long short-term memory (LSTM), so that the girth welds can be well assessed. Finally, an experiment is conducted using pipeline girth weld X-ray images. The analytic hierarchy process (AHP) method is used to comprehensively assess the level of overall girth-weld defects. The experimental results show that the proposed method is effective in recognizing and evaluating the girth-weld defects of X-ray images. And the accuracy of the quality evaluator can reach 92% for the quality assessment of overall girth welds, compared with manual quality decisions. It effectively achieves online quality assessment for girth welds and has higher evaluation accuracy.