培训人工智能识别对盲人和视力低下社区感兴趣的对象
概括
定制的物体检测模型有利于失明或视力低下的人 (pBLV). 用以用户为中心的数据训练人工智能显著提高了识别日常生活对象的性能,增强了辅助技术.
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:
- 对象检测模型为盲人或视力低下者 (pBLV) 提供了潜力.
- 现有的模型是基于通用的数据进行训练的,这限制了它们对于pBLV特定需求的实用性.
- 定制数据集对于为pBLV开发有效的辅助AI至关重要.
研究的目的:
- 开发和评估一个对象检测模型,在专门为pBLV的需求策划的数据集上进行训练.
- 确定与pBLV日常运作相关的关键对象.
- 调查数据集策划,特别是类不平衡修剪对模型性能的影响.
主要方法:
- 以用户为中心的研究 (采访,问卷,ORBIT) 确定了35个与pBLV相关的对象.
- 这些物体的图像来源于谷歌开放图像V6数据集.
- 在策划的数据集上训练了一个YOLOv5x模型,特别注意对阶级失衡进行修剪.
主要成果:
- 定制模型成功地识别了通用模型 (例如咖啡杯,刀,叉子,玻璃) 遗漏的日常功能对象.
- 仔细的数据集修剪导致模型性能提高了两倍 (mAP50-95从代表性不足的类从0.14到0.36).
- 在数据整理后,观察到更好的训练速度和对象检测结果.
结论:
- 定制训练数据对于提高pBLV对象检测模型性能至关重要.
- 以用户为中心的数据策划提高了人工智能在辅助技术中的有效性.
- 这种方法为开发专注于pBLV社区特定需求的AI提供了明确的途径.
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