模拟空中无人机图像上的黑子深度学习探测器的语言解释
Brendan Alvey1, Derek Anderson1, James Keller1
1Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211, USA.
Sensors (Basel, Switzerland)
|August 12, 2023
概括
本研究引入了一种分层方法,用于在空中图像中生成和评估深度学习模型的语言总结. 这种方法有效地识别了以人为中心的分析模型的成功,缺陷和偏见.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
- 计算机视觉 计算机视觉
背景情况:
- 深度学习模型越来越多地用于空中图像分析.
- 了解和解释这些复杂模型的行为是至关重要的.
- 现有的可解释AI (XAI) 方法用于生成语言总结,由于众多数据属性,它们面临着可扩展性的挑战.
研究的目的:
- 提出一种层次化的方法来生成和评估黑子深度学习模型的语言陈述.
- 根据用户定义的标准,开发一种评分和排名这些陈述的方法.
- 展示一种高效,以人为中心的流程,用于识别模型性能和偏差.
主要方法:
- 概述了一种系统的过程,用于评估低海拔空中无人机数据集上的物体探测器.
- 在真实空中图像上训练的深度学习模型使用具有已知的地面真相的光现实模拟数据集进行了评估.
- 提出的等级方法被用来生成和排序针对不同类型的用户量身定制的语言摘要.
主要成果:
- 层次方法有效地生成和评估深度学习模型的语言陈述.
- 该方法通过为各种用户需求制作量身定制的摘要来证明其多功能性.
- 对具有已知的基本真相的模拟数据的评估强调了该方法识别模型性能特征的能力.
结论:
- 提出的等级方法提供了一种高效和以人为中心的方法,用于解释空中图像分析中的深度学习模型.
- 这种技术有助于识别模型的成功,缺陷和偏见.
- 该方法具有多功能性,可以适应不同的用户需求和分析环境.
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