为解决变电设备锈蚀数据集标注过程中经常遭遇的模糊和不确定问题,提出了一种基于分层嵌套的训练样本标注方法.首先,采用较大的矩形框对锈蚀区域进行大面积标注,将视觉上连续的、相邻的或不能清晰划分的锈蚀区域用一个矩形框标注;然后,在标注框内对特征明显并具有相对独立性的区域进行二次标注,形成第2层内部嵌套标注.为了验证分层嵌套方法的有效性,与常用标记方法进行对比实验.结果 表明,采用分层嵌套标注方法后,YOLOv5模型的召回率由50.79％提升至59.41％,Faster R-CNN+VGG16模型的召回率由66.50％提升至78.94％,Faster R-CNN+ Res101模型的召回率由78.32％提升至84.61％.由此可见,通过分层嵌套标注可以有效提升主流模型在金属锈蚀方面的检测能力.“,”To solve the ambiguity and uncertainty in the labeling process of power equipment corrosion datasets,a novel hierarchical annotation method (HAM) is proposed.Firstly,large boxes are used to label a large area covering the range of corrosion,provided that the area is visually continuous and adjacent to corrosion that cannot be clearly divided.Secondly,in each labeling box established in the first step,regions with distinct corrosion and relative independence are labeled to form a second layer of nested boxes.Finally,a series of comparative experiments are conducted with other common annotation methods to validate the effectiveness of HAM.The experimental results show that,with the help of HAM,the recall of YOLOv5 increases from 50.79％ to 59.41％ ;the recall of Faster R-CNN + VGG16 increases from 66.50％ to 78.94％ ;the recall of Faster R-CNN + Res101 increases from 78.32％ to 84.61％.Therefore,HAM can effectively improve the detection ability of mainstream models in detecting metal corrosion.