Transmission line detection technology is crucial for automatic monitoring and ensuring the safety of electrical facilities. The YOLOv5 series is currently one of the most advanced and widely used methods for object detection. However, it faces inherent challenges, such as high computational load on devices and insufficient detection accuracy. To address these concerns, this paper presents an enhanced lightweight YOLOv5 technique customized for mobile devices, specifically intended for identifying objects associated with transmission lines. The C3Ghost module is integrated into the convolutional network of YOLOv5 to reduce floating point operations per second (FLOPs) in the feature channel fusion process and improve feature expression performance. In addition, a FasterNet module is introduced to replace the c3 module in the YOLOv5 Backbone. The FasterNet module uses Partial Convolutions to process only a portion of the input channels, improving feature extraction efficiency and reducing computational overhead. To address the imbalance between simple and challenging samples in the dataset and the diversity of aspect ratios of bounding boxes, the wIoU v3 LOSS is adopted as the loss function. To validate the performance of the proposed approach, Experiments are conducted on a custom dataset of transmission line poles. The results show that the proposed model achieves a 1% increase in detection accuracy, a 13% reduction in FLOPs, and a 26% decrease in model parameters compared to the existing YOLOv5.In the ablation experiment, it was also discovered that while the Fastnet module and the CSghost module improved the precision of the original YOLOv5 baseline model, they caused a decrease in the [email protected] metric. However, the improvement of the wIoUv3 loss function significantly mitigated the decline of the [email protected] metric.

翻译：输电线路检测技术对电力设施的自动监控与安全保障至关重要。YOLOv5系列是目前最先进且应用最广泛的目标检测方法之一，但其面临设备计算负载高、检测精度不足等固有挑战。针对这些问题，本文提出一种面向移动设备的增强型轻量化YOLOv5技术，专门用于识别与输电线路相关的目标。该方法在YOLOv5的卷积网络中集成C3Ghost模块，以降低特征通道融合过程中的每秒浮点运算次数（FLOPs），并提升特征表达能力。此外，引入FasterNet模块替代YOLOv5骨干网络中的C3模块。FasterNet模块通过部分卷积仅处理部分输入通道，提高了特征提取效率并减少了计算开销。为解决数据集中简单样本与困难样本的不平衡问题以及边界框宽高比的多样性，采用wIoU v3 LOSS作为损失函数。基于输电线路杆塔自定义数据集的实验验证了所提方法的性能：与现有YOLOv5相比，所提模型实现了检测精度提升1%、FLOPs降低13%、模型参数量减少26%。消融实验还发现，尽管Fastnet模块与C3Ghost模块提升了原始YOLOv5基线模型的精确率，但导致[email protected]指标下降；而wIoUv3损失函数的改进显著缓解了该指标的下降程度。