An X-Ray-Based Automatic Welding Defect Detection Method for Special Equipment System

In the special equipment systems, welding quality assessment based on X-ray flaw detection is related to the industrial production safety. As a key component in weld quality monitoring, automatic defect detection system aims to locate and identify numerous welding defects (e.g., air holes and cracks). Some current defect detection methods are difficult to accurately detect dense and small defects in low-quality X-ray images. In order to tackle the current challenges, this article proposes an effective weld defect detection method, termed integrated and iterative deep network (I2D-Net). First, the original weld image is decomposed by using frequency domain filtering to make full utilization of high- and low-frequency information and enhance the weak defect features. Second, a parallel detection network based on residual connection blocks of different depth and width is designed, which can flexibly capture defect features from multiple perspectives. Then, an integrated and iterative model prediction method is proposed to better extract the feature representation of small defects, thus effectively improving the overall performance. In the experiment, we built a hardware testing platform and conducted a large number of defect detection experiments and analyses. In addition, we also elaborated on practical application cases to prove that the proposed method has high industrial application value. Finally, the evaluation results of the datasets of weld defects of pressure pipeline and vessel show that the proposed I2D-Net is superior to the state-of-the-art (SoTA) methods in terms of average precision (AP increased by 7.7%, recall increased by 11.5%, and detection rate reached 98.4%).