Improvement and Enhancement of YOLOv5 Small Target Recognition Based on
  Multi-module Optimization
Qingyang Li Yuchen Li Hongyi Duan JiaLiang Kang Jianan Zhang Xueqian Gan Ruotong Xu
In this paper, the limitations of YOLOv5s model on small target detection
task are deeply studied and improved. The performance of the model is
successfully enhanced by introducing GhostNet-based convolutional module,
RepGFPN-based Neck module optimization, CA and Transformer's attention
mechanism, and loss function improvement using NWD. The experimental results
validate the positive impact of these improvement strategies on model
precision, recall and mAP. In particular, the improved model shows significant
superiority in dealing with complex backgrounds and tiny targets in real-world
application tests. This study provides an effective optimization strategy for
the YOLOv5s model on small target detection, and lays a solid foundation for
future related research and applications.