培训室内和特定场景的语义细分模型,以帮助盲人和视力低下的用户进行日常生活活动
Ruijie Sun1, Giles Hamilton-Fletcher2, Sahil Faizal3
1Computer Science Department, Courant Institute of Mathematical SciencesNew York University New York NY 10012 USA.
IEEE open journal of engineering in medicine and biology
|November 12, 2025
概括
特定场景的人工智能模型显著改善盲人或视力低下者 (pBLV) 的对象识别,增强日常生活辅助. 这些模型在各种室内环境和照明条件下更有效和更有弹性.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 辅助技术 辅助技术 辅助技术
背景情况:
- 失明或视力低下 (pBLV) 的人在日常生活任务中遇到困难.
- 智能手机上的语义细分为对象识别提供了潜力,但其在各种室内环境中的有效性尚不清楚.
研究的目的:
- 评估特定场景的人工智能模型与与日常生活活动相关的对象识别 (ADLs/IADLs) 的通用模型对pBLV的性能.
- 评估模型在不同的室内环境和照明条件中的性能.
主要方法:
- 在MIT ADE20K SceneParse150数据集上训练和评估AI模型 (DeepLabV3+).
- 对比特定场景的模型 (厨房,卧室,浴室,客厅) 与通用的室内模型.
- 使用平均准确度和交叉对联度指标评估性能.
主要成果:
- 与通用模型相比,特定于场景的模型在识别ADL/IADL对象方面表现优越.
- 为具有更多独特物体 (卧室,浴室) 的房间训练的模型显示出最显著的改进.
- 特定场景的模型在低光条件下表现出更大的弹性.
结论:
- 使用特定场景的人工智能模型可以提高在各种功能环境中协助pBLV的性能指标.
- 动态选择最佳的移动AI模型可以改善对pBLV的ADL/IADL促进.
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