In image-based pipe defect detection research, the effective utilization of the information in the two-dimension (2D) image is directly related to the sampling of the image. The existing inspection methods do not analyze the pipeline imaging but rather directly use the object detection method for defect detection, resulting in a bottleneck problem for the accuracy. In this study, the pipeline imaging was analyzed. It was found that effective sampling of the defect texture within the edge region of the image could improve defect detection accuracy. An image sampling framework, pipe-extended feature pyramid network (P-EFPN), was constructed, and the super-resolution (SR) module was embedded for texture extraction to obtain rich defect texture information and provide image sampling support for pipe defect detection. The defect dataset contains deformation, corrosion, and crack. In the faster region-convolutional neural network (R-CNN) model with Resnet-101 as the backbone, the mean average precision (mAP) of the P-EFPN model was improved by 8.64% compared to the state-of-the-art feature pyramid network (FPN) model. The proposed method improves the accuracy of defect detection by capturing more textures in the edge regions of the image. Compared with existing image sampling methods, the proposed sampling method is more suitable for pipe defect detection.

在基于图像的管道缺陷检测研究中，二维（2D）图像中信息的有效利用与图像的采样直接相关。现有的检测方法不分析管道成像，而是直接使用物体检测方法进行缺陷检测，导致精度存在瓶颈问题。本研究对管道成像进行了分析。研究发现，对图像边缘区域内的缺陷纹理进行有效采样可以提高缺陷检测的准确性。构建了图像采样框架——管道扩展特征金字塔网络（P-EFPN），并嵌入超分辨率（SR）模块进行纹理提取，以获得丰富的缺陷纹理信息，为管道缺陷检测提供图像采样支持。缺陷数据集包含变形、腐蚀和裂纹。在以 Resnet-101 为骨干的更快的区域卷积神经网络 (R-CNN) 模型中，P-EFPN 模型的平均精度 (mAP) 与现有技术相比提高了 8.64%特征金字塔网络（FPN）模型。所提出的方法通过在图像的边缘区域捕获更多的纹理来提高缺陷检测的准确性。与现有的图像采样方法相比，本文提出的采样方法更适用于管道缺陷检测。所提出的方法通过在图像的边缘区域捕获更多的纹理来提高缺陷检测的准确性。与现有的图像采样方法相比，本文提出的采样方法更适用于管道缺陷检测。所提出的方法通过在图像的边缘区域捕获更多的纹理来提高缺陷检测的准确性。与现有的图像采样方法相比，本文提出的采样方法更适用于管道缺陷检测。