Few-Shot Surface Damage Detection of Wire Rope Using Improved Meta-Learning

doi:10.3778/j.issn.1002-8331.2401-0359

Abstract

Abstract: Addressing the challenges associated with the difficulty of constructing a dataset encompassing diverse forms of surface damage on wire ropes and the uniformity in data volume, this paper introduces a novel approach for few-shot surface damage detection in wire ropes, leveraging an enhanced meta-learning framework. Within this methodology, deformable convolution is employed in lieu of conventional convolution within the query branch, facilitating the extraction of more nuanced spatial information. Additionally, it incorporates a bidirectional feature pyramid network in the neck network to amalgamate the query features. In the design of the support branch, a global refinement network is formulated to derive category feature vectors from the supporting images, and a conditional pooling mechanism is implemented to compress the feature images. To effectively extract regions of interest in query samples, a multi-scale weighted module is introduced, utilizing an improved channel attention-coordinate attention approach, and the output obtained through the coordinate attention is fed into the Re-SPPD module. Furthermore, a dedicated small target detection layer is devised to discern the damage condition of smaller entities with heightened precision. Experimental findings demonstrate that, in comparison to alternative methodologies, the proposed approach attains an average mean average precision (mAP) of 76.15% and 83.25% for 5-shot and 10-shot tasks, respectively, on the wire rope dataset. These results significantly outperform other methods, affirming the efficacy of the proposed methodology.

Key words: meta-learning, few-shot learning, deformation convolution, coordinate attention, wire rope, small target detection layer

摘要： 为了解决构建包含不同形式钢丝绳表面损伤的数据集的困难以及数据量的一致性所带来的挑战，引入了一种基于改进元学习的小样本钢丝绳表面损伤检测的新方法。在该方法中，在查询分支中使用可变形卷积代替传统卷积，从而便于提取更细微的空间信息。此外，在颈部网络中加入双向特征金字塔网络来合并查询特征。在支持分支的设计中，构建了全局精细化网络，从支持图像中提取类别特征向量，并采用自适应条件池化机制对特征图像进行压缩。为了有效地提取查询样本中的感兴趣区域，引入了一个多尺度重加权模块，利用改进的ECA通道注意力-坐标注意力方法，并且将经过坐标注意力得到的输出送入到Re-SPPD模块。在此基础上，设计了专用的小目标检测层，提高了识别小目标损伤情况的精度。实验结果表明，与其他方法相比，该方法在钢丝绳数据集上的5-shot，10-shot任务的平均精度（mAP）分别为76.15%和83.25%。这些结果明显优于其他方法，肯定了所提出的方法的有效性。

关键词: 元学习, 小样本学习, 变形卷积, 坐标注意力, 钢丝绳, 小目标检测层

HUANG Yi, GUO Jinmeng, REN Ziyu, ZHANG Jinlai, REN Guang’an, FU Ling. Few-Shot Surface Damage Detection of Wire Rope Using Improved Meta-Learning[J]. Computer Engineering and Applications, 2025, 61(13): 256-269.

黄毅, 郭金梦, 任子誉, 张金来, 任广安, 付玲. 基于改进元学习的小样本钢丝绳表面损伤检测[J]. 计算机工程与应用, 2025, 61(13): 256-269.

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