Traffic light recognition is essential to intelligent vehicle information perception, and its accuracy directly affects traffic safety. Based on sorting out and analyzing the existing related research on traditional machine learning and deep learning in traffic light recognition, the basic principles of two standard algorithms in meta-learning are introduced in detail. Compared with traditional machine learning, using the MAML algorithm and Reptile algorithms in meta-learning to recognize traffic lights by selecting the WPI dataset and programming in PYTHON. The simulation results show that the recognition accuracy based on the meta-learning algorithm is higher than that based on the traditional machine learning algorithm; In the meta-learning algorithm, the Reptile algorithm outperforms the MAML algorithm.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.