In our increasingly diverse society, everyday physical interfaces often present barriers, impacting individuals across various contexts. This oversight, from small cabinet knobs to identical wall switches that can pose different contextual challenges, highlights an imperative need for solutions. Leveraging low-cost 3D-printed augmentations such as knob magnifiers and tactile labels seems promising, yet the process of discovering unrecognized barriers remains challenging because disability is context-dependent. We introduce AccessLens, an end-to-end system designed to identify inaccessible interfaces in daily objects, and recommend 3D-printable augmentations for accessibility enhancement. Our approach involves training a detector using the novel AccessDB dataset designed to automatically recognize 21 distinct Inaccessibility Classes (e.g., bar-small and round-rotate) within 6 common object categories (e.g., handle and knob). AccessMeta serves as a robust way to build a comprehensive dictionary linking these accessibility classes to open-source 3D augmentation designs. Experiments demonstrate our detector's performance in detecting inaccessible objects.

翻译：在日益多元化的社会中，日常物理界面常常构成障碍，对各类人群的日常活动产生影响。从过小的橱柜把手到可能引发不同情境挑战的相同墙壁开关，这些设计上的疏忽凸显了寻找解决方案的迫切需求。利用低成本3D打印增强物（如旋钮放大器和触觉标签）似乎前景可期，但由于无障碍问题具有情境依赖性，发现未被识别的障碍仍是一项挑战。我们提出AccessLens系统，这是一个端到端的系统，旨在识别日常物品中难以接近的界面，并推荐可3D打印的增强方案以提升无障碍性。我们的方法包括使用专为自动识别6个常见物品类别（如把手和旋钮）中21种不同无障碍性问题类别（如bar-small和round-rotate）而设计的新型AccessDB数据集来训练检测器。AccessMeta提供了一种可靠的方式，用于构建将这些问题类别与开源3D增强设计相关联的综合字典。实验验证了我们的检测器在识别难以接近物品方面的性能。