Abstract
This paper presents an improved CNN-based structure to recognize the traffic signs. In the conventional CNN structure, only features extracted by the last convolutional layer are fed into the first fully connected layer. A recently proposed method for using more features, the MS-CNN uses features extracted by all convolutional layers, but this method may ignore the differences among layers. In our improved structure, the previous convolutional feature maps are combined to the first fully connected layer by a dynamic priority algorithm, which is inspired by the dynamic priority scheduling in the operating system. Our improved structure assigns different priority weights for certain convolutional layers, and priority is dynamically changing. This structure emphases differences among various convolutional layers and trains the network from different scales of features with assigned priorities. The results on the GTSRB dataset show that our method achieves recognition accuracy up to 97.61%, which is a considerable improvement compared to the conventional CNN and the MS-CNN.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.