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Detecting and recognizing traffic signs automatically is vital for efficiently managing traffic-sign inventory with minimal human intervention. While existing methods in the computer vision field excel at recognizing signs pertinent to advanced driver-assistance and autonomous systems, they cover only a fraction of the total traffic sign categories, leaving a significant portion unaddressed. This gap poses a challenge for automating traffic-sign inventory management, which requires handling a broader range of signs. In our study, we tackle this challenge by employing a convolutional neural network (CNN) approach, specifically the mask R-CNN, to handle the entire detection and recognition process in a seamless manner. We propose several enhancements to improve the detection performance, which we evaluate on a dataset comprising 200 different traffic sign categories, including those not previously explored. These findings indicate the feasibility of deploying our approach in real-world applications for traffic-sign inventory management.
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Copyright © 2024, This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC-BY-NY-SA). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Corresponding Author: K Siva Venkata Madhav, satishnallamalli@gmail.com
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Conflict of interest: The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s note: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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