【目的】长输油气管道环焊缝射线底片是完整性评价与风险管控的核心依据之一,底片上的焊缝序列号、依据标准、焊缝位置标记等标志信息需实现数字化归档。传统人工判读方式工作量大、效率低、成本高,且易因视觉疲劳导致漏判、误判,亟需...【目的】长输油气管道环焊缝射线底片是完整性评价与风险管控的核心依据之一,底片上的焊缝序列号、依据标准、焊缝位置标记等标志信息需实现数字化归档。传统人工判读方式工作量大、效率低、成本高,且易因视觉疲劳导致漏判、误判,亟需研发一种兼顾识别精度与模型轻量化的智能识别方法。【方法】以YOLO(You Only Look Once)v11n为基准模型,构建面向焊缝底片标志检测的YOLO-MPWR(You Only Look Once for the Marks of Pipeline Weld Radiographs)模型,并实施以下3项关键改进措施:①设计卷积门控线性单元转换器CFCGLU(ConvFormer with Convolutional Gated Linear Unit),并嵌入C3k2模块,再利用门控机制动态分配特征权重,强化对关键字符区域的响应,抑制背景及遮挡噪声;②设计了轻量化检测头LDH(Lightweight Detection Head),采用深度可分离卷积替代标准卷积,在保持精度的同时,显著减少模型参数、降低复杂度;③引入采样算子CARAFE(Content-Aware ReAssembly of FEatures),增强了YOLO-MPWR模型对重要特征的响应、特征图语义信息的利用。【结果】以中国华南地区某长输管道焊缝射线底片为数据集进行训练验证,与YOLOv11n基准模型相比,YOLO-MPWR模型的平均精度均值(mean Average Precision,mAP)mAP@0.50提升2.5%,参数量、计算量分别降低17.2%、18.2%;与RT-DETR(Real-Time Detection Transformer)、YOLOv3tiny、YOLOv5n等9种主流模型相比,YOLO-MPWR模型在精度、参数量、计算复杂度3个方面均实现了最优,在重叠、遮挡、翻转等复杂工况下漏检率更低,且对目标边缘及不规则形状区域关注更均匀。【结论】YOLO-MPWR模型在管道焊缝射线底片标志识别中实现了“高精度+超轻量”协同突破,可满足现场实时检测需求,为管道完整性数字化管理提供了可复制的技术路径,可应用于油气站场、炼化装置、船舶焊缝等工业影像目标检测场景,具有极好的工程推广价值。展开更多
There are many flaws in welding images such as noise, low contrast, and blurred edges, which affect feature extraction from welding defect regions and impede classification and recognition of welding defects. To deal ...There are many flaws in welding images such as noise, low contrast, and blurred edges, which affect feature extraction from welding defect regions and impede classification and recognition of welding defects. To deal with the complexity of welding defect images, this paper proposes an effective method for extracting the features of welding defect regions. Firstly, image preprocessing, image segmentation and image background removal are carried out to a welding image in order to extract welding defect region; and then an 8-connected-component labeling method is used to mark defect regions. Finally, it extracts geometric characteristic parameters including perimeter, area, circularity and others. The experimental result shows that the method proposed in the paper can accurately extract the features of welding defect regions. It has good adaptability and practicability.展开更多
文摘【目的】长输油气管道环焊缝射线底片是完整性评价与风险管控的核心依据之一,底片上的焊缝序列号、依据标准、焊缝位置标记等标志信息需实现数字化归档。传统人工判读方式工作量大、效率低、成本高,且易因视觉疲劳导致漏判、误判,亟需研发一种兼顾识别精度与模型轻量化的智能识别方法。【方法】以YOLO(You Only Look Once)v11n为基准模型,构建面向焊缝底片标志检测的YOLO-MPWR(You Only Look Once for the Marks of Pipeline Weld Radiographs)模型,并实施以下3项关键改进措施:①设计卷积门控线性单元转换器CFCGLU(ConvFormer with Convolutional Gated Linear Unit),并嵌入C3k2模块,再利用门控机制动态分配特征权重,强化对关键字符区域的响应,抑制背景及遮挡噪声;②设计了轻量化检测头LDH(Lightweight Detection Head),采用深度可分离卷积替代标准卷积,在保持精度的同时,显著减少模型参数、降低复杂度;③引入采样算子CARAFE(Content-Aware ReAssembly of FEatures),增强了YOLO-MPWR模型对重要特征的响应、特征图语义信息的利用。【结果】以中国华南地区某长输管道焊缝射线底片为数据集进行训练验证,与YOLOv11n基准模型相比,YOLO-MPWR模型的平均精度均值(mean Average Precision,mAP)mAP@0.50提升2.5%,参数量、计算量分别降低17.2%、18.2%;与RT-DETR(Real-Time Detection Transformer)、YOLOv3tiny、YOLOv5n等9种主流模型相比,YOLO-MPWR模型在精度、参数量、计算复杂度3个方面均实现了最优,在重叠、遮挡、翻转等复杂工况下漏检率更低,且对目标边缘及不规则形状区域关注更均匀。【结论】YOLO-MPWR模型在管道焊缝射线底片标志识别中实现了“高精度+超轻量”协同突破,可满足现场实时检测需求,为管道完整性数字化管理提供了可复制的技术路径,可应用于油气站场、炼化装置、船舶焊缝等工业影像目标检测场景,具有极好的工程推广价值。
基金supported by Special Program for Trend Setting Research of Jiangsu Province(Grant No.BY2015065-07)Research Foundation of Jiangsu Key Laboratory of Recycling and Reusing Technology for Mechanical and Electronic Products(Grant No.RRME-KF1605)
文摘There are many flaws in welding images such as noise, low contrast, and blurred edges, which affect feature extraction from welding defect regions and impede classification and recognition of welding defects. To deal with the complexity of welding defect images, this paper proposes an effective method for extracting the features of welding defect regions. Firstly, image preprocessing, image segmentation and image background removal are carried out to a welding image in order to extract welding defect region; and then an 8-connected-component labeling method is used to mark defect regions. Finally, it extracts geometric characteristic parameters including perimeter, area, circularity and others. The experimental result shows that the method proposed in the paper can accurately extract the features of welding defect regions. It has good adaptability and practicability.
