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Detection of Bus Driver Mobile Phone Usage Using Kolmogorov-Arnold Networks

Computers, ISSN: 2073-431X, Vol: 13, Issue: 9
2024
  • 1
    Citations
  • 0
    Usage
  • 13
    Captures
  • 2
    Mentions
  • 0
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

  • Citations
    1
  • Captures
    13
  • Mentions
    2
    • Blog Mentions
      1
      • Blog
        1
    • News Mentions
      1
      • News
        1

Most Recent News

Researcher at Szechenyi Istvan University Releases New Data on Computer Science (Detection of Bus Driver Mobile Phone Usage Using Kolmogorov-Arnold Networks)

2024 SEP 25 (NewsRx) -- By a News Reporter-Staff News Editor at Network Daily News -- Investigators discuss new findings in computer science. According to

Article Description

This research introduces a new approach for detecting mobile phone use by drivers, exploiting the capabilities of Kolmogorov-Arnold Networks (KAN) to improve road safety and comply with regulations prohibiting phone use while driving. To address the lack of available data for this specific task, a unique dataset was constructed consisting of images of bus drivers in two scenarios: driving without phone interaction and driving while on a phone call. This dataset provides the basis for the current research. Different KAN-based networks were developed for custom action recognition tailored to the nuanced task of identifying drivers holding phones. The system’s performance was evaluated against convolutional neural network-based solutions, and differences in accuracy and robustness were observed. The aim was to propose an appropriate solution for professional Driver Monitoring Systems (DMS) in research and development and to investigate the efficiency of KAN solutions for this specific sub-task. The implications of this work extend beyond enforcement, providing a foundational technology for automating monitoring and improving safety protocols in the commercial and public transport sectors. In conclusion, this study demonstrates the efficacy of KAN network layers in neural network designs for driver monitoring applications.

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