Peng Lincong, Wang Kerui, Zhou Hao, Ma Yi, Yu Pengfei
School of Information, Yunnan University, Kunming, 650504, China.
Yunnan Power Grid Corporation, Kunming, 650220, China.
Sci Rep. 2025 Jan 10;15(1):1635. doi: 10.1038/s41598-024-81386-y.
Detecting bolt defects on transmission lines is crucial for ensuring the safe operation of the electrical power system. However, existing methods for detecting bolt defects on transmission lines require higher detection accuracy and smaller model sizes. To address these challenges, this paper proposes a real-time bolt defect detection model based on YOLOv7, named YOLOv7-CWFD. The model integrates the Channel Shuffle Diverse Path Aggregation Network (CSDPAN), significantly reducing computational and parameter complexity while maintaining high detection accuracy. Additionally, weighted Efficient Intersection over Union (EIoU) and Normalized Wasserstein Distance (NWD) loss functions are designed to reduce the network's sensitivity to object size variations and enhance model convergence in regression tasks. The Fast Fourier Channel Attention Mechanism (FFCAM) is introduced between the backbone and neck fusion networks to mitigate excessive smoothing of detailed information and improve the network's sensitivity to objects. The DySample upsampling operator is implemented to replace the upsampling module in the neck fusion network, minimizing information loss during the upsampling process. Experiments conducted on the custom Transmission Line Bolt Defect Dataset (TLBDD) demonstrate a reduction of 10.30MB in model parameter size, along with a 2.30% increase in mean Average Precision (mAP) compared with the original YOLOV7 and a detection speed of 51.15 frames per second (FPS). Experiments on the public dataset CCTSDB further confirm the model's robust generalization capability. These experiments validate the effectiveness of the proposed algorithm.
检测输电线路上的螺栓缺陷对于确保电力系统的安全运行至关重要。然而，现有的输电线路螺栓缺陷检测方法需要更高的检测精度和更小的模型尺寸。为应对这些挑战，本文提出了一种基于YOLOv7的实时螺栓缺陷检测模型，名为YOLOv7-CWFD。该模型集成了通道混洗多样路径聚合网络（CSDPAN），在保持高检测精度的同时显著降低了计算和参数复杂度。此外，设计了加权高效交并比（EIoU）和归一化瓦瑟斯坦距离（NWD）损失函数，以降低网络对物体尺寸变化的敏感度，并增强回归任务中的模型收敛性。在主干网络和颈部融合网络之间引入了快速傅里叶通道注意力机制（FFCAM），以减轻详细信息的过度平滑，并提高网络对物体的敏感度。实现了DySample上采样算子来替换颈部融合网络中的上采样模块，最小化上采样过程中的信息损失。在自定义的输电线路螺栓缺陷数据集（TLBDD）上进行的实验表明，与原始YOLOV7相比，模型参数大小减少了10.30MB，平均精度均值（mAP）提高了2.30%，检测速度为每秒51.15帧（FPS）。在公共数据集CCTSDB上的实验进一步证实了该模型强大的泛化能力。这些实验验证了所提算法的有效性。
