利用眼动跟踪评估损失评估中的人类专家知识:灾难案例研究

Muhammad Rakeh Saleem, Robert Mayne, Rebecca Napolitano
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引用次数: 0

摘要

自然灾害日益频繁,需要高效、准确的结构损坏评估,以确保公共安全并加快灾后恢复。人为错误、标准不统一和安全风险限制了工程师进行传统的目视检查。虽然无人机和人工智能推进了灾后评估,但它们仍然缺乏人类检查员的专业知识和决策判断。本研究通过使用眼动跟踪技术捕捉专家和新手检查员的注视模式,探讨在灾害检查过程中,专业知识如何影响人类与建筑物之间的互动。研究采用了一种可控的、基于屏幕的检查方法来安全地收集数据,然后利用这些数据来训练一个用于突出图预测的机器学习模型。结果凸显了专家和新手在视觉注意力方面的显著差异,为未来的检测策略和培训新手检测员提供了宝贵的见解。通过将人类专业知识与自动化系统相结合,这项研究旨在提高灾后结构评估的准确性和可靠性,从而在救灾工作中促进更有效的人机协作。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Evaluating Human Expert Knowledge in Damage Assessment Using Eye Tracking: A Disaster Case Study
The rising frequency of natural disasters demands efficient and accurate structural damage assessments to ensure public safety and expedite recovery. Human error, inconsistent standards, and safety risks limit traditional visual inspections by engineers. Although UAVs and AI have advanced post-disaster assessments, they still lack the expert knowledge and decision-making judgment of human inspectors. This study explores how expertise shapes human–building interaction during disaster inspections by using eye tracking technology to capture the gaze patterns of expert and novice inspectors. A controlled, screen-based inspection method was employed to safely gather data, which was then used to train a machine learning model for saliency map prediction. The results highlight significant differences in visual attention between experts and novices, providing valuable insights for future inspection strategies and training novice inspectors. By integrating human expertise with automated systems, this research aims to improve the accuracy and reliability of post-disaster structural assessments, fostering more effective human–machine collaboration in disaster response efforts.
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