Abstract:

Deep learning-based foreign object detection methods for transmission lines have become a critical way for continuous and stable operation of next-generation power grids. To enhance detection performance for foreign objects on power transmission lines， a hypergraph-based global-perspective position-aware foreign object detection model was proposed using Hyper-YOLO’s ability to model and propagate high-order semantic correlations across levels and positions. Firstly， a multi-path feature aggregation network with position awareness — PMFA （Position-aware Multi-branch Feature Aggregation） was introduced to position critical features needed to be represented. Additionally， a Global SPPF （Global Spatial Pyramid Pooling-Fast） module with a global receptive field was designed to enhance boundary features extracted by the backbone， so as to promote the modeling and learning abilities of the model to high-order semantic correlations. Finally， an attention-guided LAWD （Lightweight Attention-guided Weighted Downsampling） module was proposed to retain critical information as more as possible from low-quality data samples. The effectiveness and superiority of foreign object detection in transmission line scenarios of the proposed model were verified through ablation studies， comparative analyses， and data augmentation on the constructed dataset of foreign object images of transmission lines. Experimental results demonstrate that the proposed model achieves a precision of 90.6%， a recall of 87.6%， and a mAP@50 of 93.4%， and compared with the baseline model， it has the number of parameters reduced by 20.5%， and the floating-point operations reduced by 20.4%， providing technical support for the detection and management of foreign objects on transmission lines.

Key words: transmission line, foreign object detection, Hyper-YOLO, position awareness, global receptive field, feature representation

摘要：

基于深度学习的输电线路异物检测方法已成为确保新一代电网能持续稳定运行的重要手段之一。为了提高输电线异物检测的整体检测性能，利用Hyper-YOLO模型能对特征图中的高阶语义相关性进行跨层和跨位置的建模与传播的特性，提出一种基于超图结构的具有全局视角及位置感知特性的输电线异物检测模型。首先，构建一个具有位置感知特性的多路径特征聚合网络PMFA（Position-aware Multi-branch Feature Aggregation），以精准地定位需要表征的关键特征；此外，设计具有全局视野的Global SPPF（Global Spatial Pyramid Pooling-Fast）模块，以增强骨干网络特征提取的边缘特性强化检测模型对高阶语义相关性的建模与学习能力；最后，提出基于注意力导向的LAWD （Lightweight Attention-guided Weighted Downsampling）模块，以尽可能地保留低质量数据样本中更多的关键信息。在所构建的输电线异物图像数据集上，通过一系列的消融实验、对比实验以及数据增强的方式验证所提模型在输电线异物检测场景下的有效性及检测性能上的优越性。实验结果表明，所提模型的检测精度、召回率及mAP@50值分别达到了90.6%、87.6%和93.4%，且相较于基线模型在参数量上降低了20.5%，浮点运算量上下降了20.4%，为输电线路场景下异物目标的检测与治理提供了技术支持。

关键词: 输电线, 异物检测, Hyper-YOLO, 位置感知, 全局视野, 特征表示