Common fully glazed facades and transparent objects present architectural barriers and impede the mobility of people with low vision or blindness, for instance, a path detected behind a glass door is inaccessible unless it is correctly perceived and reacted. However, segmenting these safety-critical objects is rarely covered by conventional assistive technologies. To tackle this issue, we construct a wearable system with a novel dual-head Transformer for Transparency (Trans4Trans) model, which is capable of segmenting general and transparent objects and performing real-time wayfinding to assist people walking alone more safely. Especially, both decoders created by our proposed Transformer Parsing Module (TPM) enable effective joint learning from different datasets. Besides, the efficient Trans4Trans model composed of symmetric transformer-based encoder and decoder, requires little computational expenses and is readily deployed on portable GPUs. Our Trans4Trans model outperforms state-of-the-art methods on the test sets of Stanford2D3D and Trans10K-v2 datasets and obtains mIoU of 45.13% and 75.14%, respectively. Through various pre-tests and a user study conducted in indoor and outdoor scenarios, the usability and reliability of our assistive system have been extensively verified.

提出Trans4Trans模型，利用双头转换器来分割常见的全玻璃幕墙、透明物体，进行实时的辅助导航，有效提升低视力人士的行动能力。模型基于对称变压器编码器和解码器，花费较少计算复杂度，可在可携带的GPU上轻松部署。在Stanford2D3D和Trans10K-v2数据集上表现优于最先进方法，并获得了45.13％和75.14％的mIoU。通过各种预测试和用户研究来验证辅助系统的可用性和可靠性。

Trans4Trans: 用于透明物体分割的高效 Transformer，帮助视力障碍者在现实世界中导航