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The increasing accident rate brought about by the explosive growth ofautomobiles has made the research on active safety systems of automobilesincreasingly important. The importance of improving the accuracy of vehicletarget detection is self-evident. To achieve the goals of vehicle detection anddistance estimation and provide safety warnings, a Distance Estimation SafetyWarning System (DESWS) based on a new neural network model (YOLOv5s-SE) byreplacing the IoU with DIoU, embedding SE attention module, and a distanceestimation method through using the principle of similar triangles wasproposed. In addition, a method that can give safety suggestions based on theestimated distance using nonparametric testing was presented in this work.Through the simulation experiment, it was verified that the mAP was improved by5.5% and the purpose of giving safety suggestions based on the estimateddistance information can be achieved.

Multi-strategy Collaborative Optimized YOLOv5s and its Application in Distance Estimation