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 mAP@.5-.95 metric. However, the
improvement of the wIoUv3 loss function significantly mitigated the decline of
the mAP@.5-.95 metric.

本文介绍了一种针对移动设备的增强轻量级 YOLOv5 技术，用于识别与输电线相关的物体。该方法通过引入 C3Ghost 模块和 FasterNet 模块改进了 YOLOv5 的性能，并采用 wIoU v3 LOSS 损失函数解决了数据集中简单和挑战样本的不平衡问题。实验证明，与现有的 YOLOv5 相比，该模型在检测准确性上提高了 1%，降低了 13% 的 FLOPs，模型参数减少了 26%。而在消融实验中，Fastnet 模块和 CSghost 模块提高了原始 YOLOv5 基准模型的精度，但导致了 mAP@.5-.95 度量的下降，而 wIoUv3 损失函数的改进显著减轻了该度量的下降。