Real-Time Runway Incursion Detection Using AI-Based Sensor Fusion and Computer Vision

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@article{1717137,
      author = {Sam Suseelan},
      title = {Real-Time Runway Incursion Detection Using AI-Based Sensor Fusion and Computer Vision},
      journal = {Iconic Research And Engineering Journals},
      year = {2022},
      volume = {6},
      number = {5},
      pages = {318-326},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1717137.pdf},
      abstract = {The safety of runway operations is a major challenge in the aviation industry due to rising air traffic complexity and the shortcomings of conventional surveillance systems. Traditional methods, such as radar monitoring and manual air navigation, are prone to slow detection, poor situational awareness, and poor visibility in poor weather. This research presents an AI-powered sensor fusion and computer vision approach to detect runway incursions in real time. It uses information from CCTV cameras, radar, and IoT sensors, which are processed in the fusion layer, and are then analyzed by a deep learning object detection algorithm. A set of rules is used to detect runway zone violations and trigger alerts. The system was tested in a controlled, simulated environment. The system achieves high accuracy with 96.8% accuracy, 95.4% precision, 94.9% recall, 95.1% F1-score, 3.2% false positive rate and an average latency of 115 ms, indicating effective and timely detection. But the testing is conducted in controlled settings, and may not reflect real-world conditions at airports. In summary, the results indicate that combining AI-based computer vision with sensor fusion has the potential to improve runway safety systems, but it needs to be further evaluated in real-world deployment scenarios.},
      keywords = {Runway Incursion Detection, Computer Vision, Real-Time Systems, Aviation Safety, Smart Airport Systems},
      month = {November},
      doi = {https://doi.org/10.64388/IREV6I5-1717137}
  }