Background and Objective

Daily activities such as shopping and navigating indoors are challenging problems for people with visual impairment. Researchers tried to find different solutions to help people with visual impairment navigate indoors and outdoors.

Methods

We applied deep learning to help visually impaired people navigate indoors using markers. We propose a system to help them detect markers and navigate indoors using an improved Tiny-YOLOv3 model. A dataset was created by collecting marker images from recorded videos and augmenting them using image processing techniques such as rotation transformation, brightness, and blur processing. After training and validating this model, the performance was tested on a testing dataset and on real videos.

Results

The contributions of this paper are: (1) We developed a navigation system to help people with visual impairment navigate indoors using markers; (2) We implemented and tested a deep learning model to detect Aruco markers in different challenging situations using Tiny-YOLOv3; (3) We implemented and compared several modified versions of the original model to improve detection accuracy. The modified Tiny-YOLOv3 model achieved an accuracy of 99.31% in challenging conditions and the original model achieved an accuracy of 96.11 %.

Conclusion

The training and testing results show that the improved Tiny-YOLOv3 models are superior to the original model.

中文翻译：

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一种基于改进的 Tiny-YOLOv3 模型的视障人士新型标记检测系统

背景与目的

诸如购物和在室内导航等日常活动对视力障碍者来说是具有挑战性的问题。研究人员试图找到不同的解决方案来帮助视力障碍者在室内和室外导航。

方法

我们应用深度学习来帮助视障人士使用标记在室内导航。我们提出了一个系统来帮助他们检测标记并使用改进的 Tiny-YOLOv3 模型在室内导航。通过从录制的视频中收集标记图像并使用旋转变换、亮度和模糊处理等图像处理技术对其进行扩充来创建数据集。在训练和验证该模型之后，在测试数据集和真实视频上测试了性能。

结果

本文的贡献在于：（1）我们开发了一种导航系统，帮助视力障碍者使用标记在室内导航；(2) 我们使用 Tiny-YOLOv3 实施并测试了一个深度学习模型，以检测不同挑战性情况下的 Aruco 标记；(3) 我们实施并比较了原始模型的几个修改版本，以提高检测精度。修改后的 Tiny-YOLOv3 模型在具有挑战性的条件下实现了 99.31% 的准确度，原始模型实现了 96.11% 的准确度。

结论

训练和测试结果表明，改进后的 Tiny-YOLOv3 模型优于原始模型。