Multi-strategy Collaborative Optimized YOLOv5s and its Application in
  Distance Estimation
Zijian Shen Zhenping Mu Xiangxiang Li
The increasing accident rate brought about by the explosive growth of
automobiles has made the research on active safety systems of automobiles
increasingly important. The importance of improving the accuracy of vehicle
target detection is self-evident. To achieve the goals of vehicle detection and
distance estimation and provide safety warnings, a Distance Estimation Safety
Warning System (DESWS) based on a new neural network model (YOLOv5s-SE) by
replacing the IoU with DIoU, embedding SE attention module, and a distance
estimation method through using the principle of similar triangles was
proposed. In addition, a method that can give safety suggestions based on the
estimated distance using nonparametric testing was presented in this work.
Through the simulation experiment, it was verified that the mAP was improved by
5.5% and the purpose of giving safety suggestions based on the estimated
distance information can be achieved.