Abstract:
This research paper investigates the development
of deep learning models for traffic sign recognition in
autonomous vehicles. Leveraging convolutional neural networks
(CNNs), the study explores various architectural configurations
and evaluation methodologies to assess the efficacy of CNNs in
accurately identifying and classifying traffic signs. Through a
systematic evaluation process utilizing metrics such as accuracy,
precision, recall, and F-score, the research demonstrates the
robustness and generalization capability of the developed models
across diverse environmental conditions. Furthermore, the
utilization of visualization techniques, including the Matplotlib
library, enhances the interpretability of model training dynamics
and optimization progress. The findings highlight the significance
of CNN architecture in facilitating hierarchical feature extraction
and spatial dependency learning, thereby enabling reliable and
efficient traffic sign recognition. The successful recognition of
traffic signs under varying lighting conditions underscores the
resilience of the developed models to environmental
perturbations. Overall, this research contributes to advancing
the capabilities of autonomous vehicle systems and lays the
groundwork for the implementation of intelligent traffic sign
recognition systems aimed at enhancing road safety and
navigational efficiency.
