Research and Implementation of Traffic Sign Recognition Algorithm Model Based on Machine Learning

Abstract

Traffic sign recognition is an important task in intelligent transportation systems, which can improve road safety and reduce accidents. Algorithms based on deep learning have achieved remarkable results in traffic sign recognition in recent years. In this paper, we build traffic sign recognition algorithms based on ResNet and CNN models, respectively. We evaluate the proposed algorithm on public datasets and compare. We first use the dataset of traffic sign images from Kaggle. And then designed ResNet-based and CNN-based architectures that can effectively capture the complex features of traffic signs. Our experiments show that our ResNet-based model achieves a recognition accuracy of 99% on the test set, and our CNN-based model achieves a recognition accuracy of 98% on the test set. Our proposed approach has the potential to improve traffic safety and can be used in various intelligent transportation systems.

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“…The evolution of machine learning has fundamentally transformed a myriad of fields, showcasing its remarkable versatility and power in tackling complex problems. From traffic sign recognition 1 and cancer gene data classification 2 , to the challenges of autonomous navigation at unsignalized intersections 3 and real-world storm prediction 4 , the impact of machine learning is profound and far-reaching. These diverse applications demonstrate the capability of machine learning to not only analyze but also predict and interpret vast and complex datasets across various domains.…”

“…In transportation, for instance, machine learning models have been instrumental in improving safety and efficiency, as seen in traffic sign recognition systems 1 . Similarly, in healthcare, deep learning techniques have enabled significant advances in understanding genetic data, thereby enhancing cancer diagnosis and treatment 2 .…”