Multi-strategy Collaborative Optimized YOLOv5s and its Application in Distance Estimation
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Abstract (arXiv)
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.
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References · 4
- 2017Squeeze-and-Excitation Networkscited by 4,763
- 2020Deep Reinforcement Learning for Autonomous Driving: A Surveycited by 698
- 2019Distance-IoU Loss: Faster and Better Learning for Bounding Box Regressioncited by 454
- 2019Multivariate Rank-based Distribution-free Nonparametric Testing using Measure Transportationcited by 24
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