The use of X-ray-based non-destructive testing (NDT) methods is widespread in the task of welding defect detection. Many scholars have turned to deep-learning computer vision models for defect detection in weld radiographic images in recent years. Before model training, annotating the collected image data is often necessary. We need to use annotation information to guide the model for effective learning. However, many researchers have been focused on developing better models or refining training strategies, often overlooking the quality of data annotation. This paper delved into the impact of eight types of low-quality annotations on the accuracy of object detection models. In comparison to accurate annotations, inaccuracies in the annotated locations significantly impact model performance, while errors in category annotations have a minor effect on model performance. Incorrect location affects both the recall and precision of the model, while incorrect categorization only impacts the precision of the model. Additionally, we observed that the extent of the impact of location errors is related to the detection accuracy of individual classes, with classes having higher original detection AP experiencing more substantial decreases in AP under location errors. Finally, we analyzed the influence of annotator habits on model performance. The study examines the effects of various types of low-quality annotations on model training and their impact on individual detection categories. Annotator habits lead to the left boundary of annotated boxes being less accurate than the right boundary, resulting in a greater impact of annotations biased to the left than those biased to the right. Based on experiments and analysis, we proposed annotation guidelines for weld defect detection tasks: prioritize the quality of location annotations over category accuracy and strive to include all objects, including those with ambiguous boundaries.

基于 X 射线的无损检测 (NDT) 方法在焊接缺陷检测任务中广泛使用。近年来，许多学者转向深度学习计算机视觉模型来检测焊缝射线图像的缺陷。在模型训练之前，通常需要对收集到的图像数据进行注释。我们需要利用标注信息来指导模型进行有效的学习。然而，许多研究人员一直专注于开发更好的模型或完善训练策略，往往忽视了数据注释的质量。本文深入研究了八种低质量注释对对象检测模型准确性的影响。与准确的注释相比，注释位置的不准确会显着影响模型性能，而类别注释中的错误对模型性能的影响较小。错误的定位会影响模型的查全率和准确率，而错误的分类只会影响模型的准确率。此外，我们观察到位置错误的影响程度与各个类别的检测精度有关，具有较高原始检测 AP 的类别在位置错误下经历 AP 的大幅下降。最后，我们分析了注释者习惯对模型性能的影响。该研究检查了各种类型的低质量注释对模型训练的影响及其对单个检测类别的影响。注释者的习惯导致注释框的左边界不如右边界准确，导致偏左的注释比偏右的注释影响更大。基于实验和分析，我们提出了焊接缺陷检测任务的注释指南：将位置注释的质量优先于类别准确性，并努力包括所有对象，包括那些边界不明确的对象。