基于多尺度特征融合与改进注意力的锈蚀螺栓螺帽检测

doi:10.6040/j.issn.1671-9352.5.2025.067

摘要/Abstract

摘要： 针对输电线路巡检中螺栓螺帽目标尺寸小、数量多、且存在复杂背景遮挡的检测问题,提出具有多尺度特征融合机制和改进注意力机制的螺栓-螺母检测Transformer(bolt-nut detection Transformer, BN-DETR)算法。构建跨阶段部分连接网络(cross stage partial darknet, CSPDarknet)为主干网络的特征提取模块,通过集成局部感知、全局注意力机制和多层感知机实现多尺度特征的高效聚合,设计基于改进注意力的尺度内特征交互模块,通过动态选择关键点降低计算复杂度,同时保持全局信息交互能力,提出多层次注意力融合机制(全局、局部、像素级)提升特征表达能力。实验结果表明, BN-DETR算法的平均精度均值@50较基线算法提高3%,为电力设施微小缺陷检测提供有效的技术参考。

关键词: 螺栓螺帽, 小目标检测, 锈蚀, Transformer

Abstract: To address the detection challenges associated with bolts and nuts in transmission line inspections—such as small object sizes, large quantities, and complex background occlusions,the method of bolt-nut detection Transformer(BN-DETR)incorporating a multi-scale feature fusion mechanism and enhanced attention mechanisms is proposed. A feature extraction module is constructed utilizing the cross-stage partial darknet(CSPDarknet)as the backbone, which efficiently aggregates multi-scale features through the integration of local perception, global attention mechanisms, and multi-layer perceptron. Scale-wise feature interaction module based on improved attention is designed to dynamically select key sampling points, thereby reducing computational complexity while preserving global information exchange. A multi-level attention fusion mechanism encompassing global, local, and pixel-level attention are introduced to augment feature representation. Experimental results demonstrate that, on a self-constructed dataset of corroded bolts in transmission lines, BN-DETR achieves 3% improvement in the @50 metric relative to the baseline algorithm. The proposed method offers an effective technical reference for the detection of small defects in power infrastructure.

Key words: bolt-nut, small object detection, rust, Transformer

中图分类号:

TP391

孙迪,郭义童,任超,范海峰,张传雷. 基于多尺度特征融合与改进注意力的锈蚀螺栓螺帽检测[J]. 《山东大学学报(理学版)》, 2026, 61(1): 1-14.

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