[2312.04113] Multi-strategy Collaborative Optimized YOLOv5s and its Application in Distance Estimation
Skip to main content
We gratefully acknowledge support from the Simons Foundation, member institutions, and all contributors. Donate
arXiv logo
Cornell University Logo

Computer Science > Computer Vision and Pattern Recognition

arXiv:2312.04113 (cs)
[Submitted on 7 Dec 2023]

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

Authors:Zijian Shen, Zhenping Mu, Xiangxiang Li
View PDF
Abstract: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.
Comments: This paper contains 5 pages, 10 figures, and was accepted at 4th International Conference on Advances in Electrical Engineering and Computer Applications (AEECA2023)
Subjects: Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2312.04113 [cs.CV]
  (or arXiv:2312.04113v1 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.2312.04113

Submission history

From: Zijian Shen [view email]
[v1] Thu, 7 Dec 2023 07:59:10 UTC (684 KB)
Full-text links:

Access Paper:

  • View PDF
  • Other Formats
license icon view license
Current browse context:
cs.CV
< prev   |   next >
new | recent | 2023-12
Change to browse by:
cs

References & Citations

export BibTeX citation

Bookmark

BibSonomy logo Reddit logo

Bibliographic and Citation Tools

Bibliographic Explorer (What is the Explorer?)
Connected Papers (What is Connected Papers?)
Litmaps (What is Litmaps?)
scite Smart Citations (What are Smart Citations?)

Code, Data and Media Associated with this Article

alphaXiv (What is alphaXiv?)
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub (What is DagsHub?)
Gotit.pub (What is GotitPub?)
Hugging Face (What is Huggingface?)
Papers with Code (What is Papers with Code?)
ScienceCast (What is ScienceCast?)

Demos

Replicate (What is Replicate?)
Hugging Face Spaces (What is Spaces?)
TXYZ.AI (What is TXYZ.AI?)

Recommenders and Search Tools

Influence Flower (What are Influence Flowers?)
CORE Recommender (What is CORE?)

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)