Fostc3net:A Lightweight YOLOv5 Based On the Network Structure
  Optimization
Danqing Ma Shaojie Li Bo Dang Hengyi Zang Xinqi Dong
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.