Abstract: As the core transmission and distribution carrier of the power system, high-voltage transmission lines were closely related to the safety of the power grid. To address the problems of low efficiency and high omission rate in traditional manual inspection, a lightweight defect detection method based on a two-stage multimodal attention mechanism and dynamic feature decoupling was proposed. In the first stage, accurate localization of key components was achieved based on an improved lightweight detection network, Light-YOLO. In the second stage, precise classification and identification of defects were performed using the difference-enhanced feature network, Dual-DifferNet. In the design of Light-YOLO, a hybrid structure of hierarchical Separable Visual Transformer (SepViT) and deep Deformable Convolution Network (DCN) was introduced. By alternately stacking local perception convolution layers and global attention Transformer blocks, the modeling capability of long-range dependencies was enhanced while reducing computational cost, which effectively improved the detection accuracy of small targets such as insulators and conductor joints. For the defect classification task, Dual-DifferNet adopted a dual-branch structure, in which a Spatial-Channel Dual Attention (SCDA) module was embedded in each branch. Cross-modal feature interaction was promoted by a cross-attention mechanism, thereby improving the robustness and generalization capability of defect identification. Experimental results showed that the proposed method achieved a mean Average Precision (mAP@50) of 96.9%, which was 16.1 percentage points higher than the baseline model (YOLOv8), while the computational cost (GFLOPs) was reduced by 56.73%, fully verifying the method’s high detection accuracy, excellent computational efficiency, and deployment potential.

Key words: transmission line defect detection, hierarchical separable vision transformer, bidirectional recursive feature pyramid network, dual attention module, deformable convolution

摘要： 高压输电线路作为电力系统的核心输配电载体，它的运行状态直接关系到电网安全。针对传统人工巡检效率低、漏检率高的问题，提出一种基于视觉的两阶段多模态注意力机制与动态特征解耦的轻量化缺陷检测方法。该方法的第一阶段基于改进型轻量检测网络Light-YOLO实现关键组件的精准定位；第二阶段则利用差分特征增强网络Dual-DifferNet实现缺陷的精确分类与识别。在Light-YOLO的设计中，引入了分层可分离视觉Transformer(SepViT)与深度可变形卷积(DCN)的混合结构，通过交替堆叠局部感知卷积层与全局注意力Transformer块，在降低计算量的同时，增强了模型对长程依赖关系的建模能力，从而有效提升了绝缘子、导线接头等小目标的检测精度。针对缺陷分类任务，Dual-DifferNet采用双分支结构，在每个分支中嵌入空间-通道双重注意力(SCDA)模块，利用交叉注意力机制促进双模态特征交互，从而提高了缺陷识别的鲁棒性与泛化能力。实验结果表明，所提方法的平均精度均值(mAP@50)达到96.9%，较基准模型(YOLOv8)提升了16.1个百分点，同时计算量(GFLOPs)降低了56.73%，充分验证了该方法在保证高精度检测的同时，具备优异的计算效率与部署潜力。

关键词: 输电线路缺陷检测, 分层可分离视觉Transformer, 双向递归特征金字塔网络, 双重注意力模块, 可变形卷积