Generate Realistic Traffic Sign Image using Deep Convolutional Generative Adversarial Networks

Liu, Yanting; Chen, Rung-Ching; Dewi, Christine

doi:10.1109/dsc49826.2021.9346266

Cited by 4 publications

(1 citation statement)

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“…In Chunsheng Liu's study group [9], Color-based approaches, shape-based methods, color and shape-based methods, machine learning-based methods, and LIDAR-based methods are the five categories they divide traffic sign identification systems into. Color-based approaches are those that are primarily dependent on color information.…”

Section: Traffic Sign Recognition and Classificationmentioning

confidence: 99%

Real time road sign detection based on YOLO and Swift

Shi

Xu²

2022

International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022)

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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.

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Section: Traffic Sign Recognition and Classificationmentioning

confidence: 99%