Stacked Extreme Learning Machine with Horse Herd Optimization: A Methodology for Traffic Sign Recognition in Advanced Driver Assistance Systems
Journal Title: Mechatronics and Intelligent Transportation Systems - Year 2023, Vol 2, Issue 3
Abstract
In the quest for autonomous vehicle safety and road infrastructure management, traffic sign recognition (TSR) remains paramount. Recent advancements in accuracy across various benchmarks have been identified in the literature concerning this essential task. Such technology might remain absent in older vehicles, while integration into Advanced Driver Assistance Systems (ADAS) is common in more recent models. Yet, the capability of these systems to function proficiently under diverse driving conditions has not been widely investigated. A framework has been devised to allow a moving vehicle to detect traffic signs, targeting the enhancement of driver safety and the diminishment of accidents. The present research introduces an innovative methodology, amalgamating the extreme learning machine (ELM) method with deep-learning paradigms, in response to experimental discoveries. As a pioneering computational approach in neural network-based learning, ELM facilitates rapid training and commendable generalization. An accuracy of 95.00% was achieved by the proposed model. By utilizing the Horse Herd Optimization method (HHOA), the memory consumption is minimized in the more sophisticated approach of stacked ELM (SELM) within the deep-learning framework. This study contributes to the understanding of potential challenges that may be encountered during TSR tasks, and lays the groundwork for future investigation by proffering a diverse set of evaluations for various road scenarios. Consistency in the utilization of professional terms is maintained throughout.
Authors and Affiliations
Praveen Kumar Jayapal, Venkateswara Rao Muvva, Venkata Subbaiah Desanamukula
Keywords
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- EP ID EP732243
- DOI https://doi.org/10.56578/mits020302
- Views 72
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