Defect detection method for high-resolution weld based on wandering Gaussian and multi-feature enhancement fusion

Abstract

As the weakest point of the pipeline, the weld seam is prone to various internal defects and has great safety risks, so it is necessary to conduct a weld seam inspection. In addition, accurately detecting small-sized defects from high-resolution and low-contrast X-ray images is still a challenging task. Therefore, this paper proposes a defect detection method for high-resolution weld images, including three steps: welded extraction, weld restructuring, and defect detection. Because the resolution of the original image is high and the defect features are mainly concentrated in the weld area, it is not easy to directly defect detection. Firstly, a new irregular long weld extraction algorithm based on wandering Gaussian was designed. Secondly, because the existing welding seams aspect ratio is large and the information loss of conventional compression methods is serious, a new feature reorganization method was provided to maximize the effective information of the weld. Finally, a new method of welding defects detection method based on cross-layer feature fusion has been redesigned to take into efficiency and accuracy. The experiments show that the proposed method achieves the best detection performance on the public GDXray dataset compared with other advanced defect detection methods, from which it can locate the weld defects automatically and fast with high accuracy. It provides an effective solution for high-resolution weld defect detection.