Abstract: X-ray inspection, as an intuitive means of nondestructive testing (NDT) of pipeline weld defects, plays a key role in the prevention of pipeline safety accidents. However, it remains challenging to accurately identify tiny defects in low-grayscale, low-contrast, and dark-toned X-ray images. Therefore, an innovative method is proposed to optimize the display effect of X-ray images of pipe welds under low-light conditions, and to achieve a certain improvement in the accuracy of defect detection. Firstly, the improved network framework of Retinex-Net is introduced, and the attention mechanism residual block is added to the network to restore illumination and enhance details of low-light X-ray images, suppress noise and artifacts, and output natural and obvious distortion enhancement images, providing high-quality input for subsequent detection. Secondly, a weld positioning and feature extraction algorithm based on drift Gaussian algorithm is designed, which adaptively tracks irregular long welds and automatically crops the weld area, which significantly reduces background interference and improves processing efficiency. Finally, the welding defect detection algorithm based on cross-layer feature fusion is optimized, and a feature codec architecture based on RSU module is constructed, and the attention mechanism is integrated in the feature extraction stage to strengthen cross-layer multi-scale feature fusion, so as to improve the detection accuracy and reduce the missed detection rate.The results show that the proposed method significantly improves the performance indicators in the public GDXray dataset, which not only effectively enhances the image quality, but also realizes the high degree of automation and fast response ability of weld defect detection, which proves its efficiency and accuracy in practical application scenarios.

摘要： X射线检测作为管道焊缝缺陷无损检测（NDT）的一种直观手段，对预防管道安全事故具有关键作用。然而，从低灰度值、低对比度且色调偏暗的X射线图像中精确识别微小缺陷仍面临巨大挑战。为此，针对低光照焊缝图像的缺陷检测问题，提出了一种创新方法，聚焦于低光照条件下管道焊缝X射线图像的显示效果优化，并在缺陷检测精度上实现一定的提升。首先引入改进的Retinex-Net的网络框架，在网络中加入注意力机制残差块，对低光照X射线图像进行光照恢复与细节增强，抑制噪声与伪影，输出自然和无明显失真的增强图像，为后续检测提供高质量输入；其次设计了一种基于漂移高斯算法的焊缝定位与特征提取算法，自适应跟踪不规则长焊缝并自动裁取焊缝区域，显著降低背景干扰并提升处理效率；最后对基于跨层特征融合的焊接缺陷检测算法进行优化，构建基于RSU模块的特征编解码架构，在特征提取阶段集成注意力机制，强化跨层多尺度特征融合，从而提升检测精度并降低漏检率。研究结果显示，所提出的方法在公共GDXray数据集中显著提升了性能指标，不仅有效增强了图像质量，还实现了焊缝缺陷检测的高自动化程度与快速响应能力，证明了其在实际应用场景中的高效性与准确性。