Abstract
A self-driving car is a growing technology in India where detection of traffic sign in an unconstrained environment is a challenging task due to its small size. With the development of deep neural networks, many models for object detection have been developed. In this work, we have used a single-stage detection model YoloV4 with further improvements in detection neck for detection of traffic signs. We have used dense connections in place of normal connections of the model for better feature propagation. This improves the accuracy with less inference time. We have conducted our experiments on bench-marked Chinese traffic sign dataset, Tshigua-Tenscent 100K dataset (TT-100K). We have achieved accuracy of 94.30% with 32 FPS.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: 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.
