Multi-strategy Collaborative Optimized YOLOv5s and its Application in Distance Estimation
This work addresses vehicle safety systems for automotive applications, but it is incremental as it builds on existing YOLOv5 with minor modifications.
The paper tackled vehicle detection and distance estimation for automotive safety by proposing a modified YOLOv5s model (YOLOv5s-SE) with DIoU and SE attention, achieving a 5.5% improvement in mAP and enabling safety warnings based on estimated distances.
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.