JOURNAL ARTICLE

Autonomous traffic sign detection for self-driving car system using convolutional neural network algorithm.

  • Published In: Journal of Intelligent & Fuzzy Systems, 2024, v. 46, n. 3. P. 5975 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Yu, Zhao; Ye, Ting 3 of 3

Abstract

The article focuses on developing a convolutional neural network (CNN) based on the YOLOv8 algorithm for accurate and real-time traffic sign detection in self-driving systems. Using a custom dataset of diverse traffic sign images with manual annotations and data augmentation, the model was trained, validated, and tested to ensure robustness and generalization. Experimental results demonstrate that the YOLOv8 model achieved a mean average precision (mAP@0.5) of 91%, indicating high accuracy suitable for autonomous vehicle navigation and video-based traffic surveillance. The study acknowledges limitations related to dataset representativeness and suggests future work to expand training data diversity to improve real-world applicability.

Additional Information

  • Source:Journal of Intelligent & Fuzzy Systems. 2024/03, Vol. 46, Issue 3, p5975
  • Document Type:Article
  • Subject Area:Science
  • Publication Date:2024
  • ISSN:1064-1246
  • DOI:10.3233/JIFS-235863
  • Accession Number:176366389
  • Copyright Statement:Copyright of Journal of Intelligent & Fuzzy Systems is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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