Real time road sign detection based on YOLO and Swift

Peilin Shi; Hanxiang Xu

doi:10.1117/12.2640893

13 October 2022 Real time road sign detection based on YOLO and Swift

Peilin Shi, Hanxiang Xu

Proceedings Volume 12287, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022); 122870N (2022) https://doi.org/10.1117/12.2640893
Event: International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022), 2022, Wuhan, China

Abstract

In this research, we find out current traffic road sign detection is mostly based on high precision detection device to collect road data for machine learning to train model for autonomous driving. For most people, createML makes it easier to use and create models such as our road sign detection than using scientific equipment in the lab. If more and more people have access to machine learning, then it will cost us less to develop a model. The point of doing this research is to provide a solution to reduce the cost of future road sign detection and development models that are valuable for real-life applications, such as Carplay. We are using an easier approach from using current exist road sign pictures, and use create ML to do the model training and find out if could be potential solutions for transplanting models into phone apps to connect car device for autonomous driving.

Citation Download Citation

Peilin Shi and Hanxiang Xu "Real time road sign detection based on YOLO and Swift", Proc. SPIE 12287, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022), 122870N (13 October 2022); https://doi.org/10.1117/12.2640893

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