Achieving high accuracy of blind road condition recognition in real-time is important for helping visually impaired people sense the surrounding environment. However, existing systems are mainly designed based on general objects detection (pedestrians, vehicles, crosswalks, etc.), ignoring the safety-critical objects such as obstacles (boxes, balls, etc.) failing on the walking areas. To tackle this issue, we construct an efficient obstacle segmentation (EOS) based system with a dedicated neural network E-BiSeNet, which is capable of segmenting blind roads, performing real-time and accurate obstacle avoidance to assist people walking more safely. Firstly, E-BiSeNet rethinks the structure redundancy in network depth and computation expenses in feature aggregation, which can be readily deployed on portable GPUs. Secondly, a simple post-processing scheme max logit (ML) based on the pretrained network segmentation outputs is introduced to locate unexpected on-road obstacles. Our “E-BiSeNet ＋ML” model outperforms state-of-the-art methods on both real-world and synthetic datasets. Through various experiments conducted in outdoor scenarios, the feasibility and reliability of the EOS have been extensively verified.

实时实现盲人路况的高精度识别，对于帮助视障人士感知周围环境具有重要意义。然而，现有系统主要是基于一般物体检测（行人、车辆、人行横道等）设计的，而忽略了行走区域上的障碍物（盒子、球等）等安全关键物体。为了解决这个问题，我们构建了一个基于高效障碍物分割（EOS）的系统，该系统具有专用的神经网络 E-BiSeNet，能够分割盲路，实时准确地避障，帮助人们更安全地行走。首先，E-BiSeNet 重新考虑了网络深度的结构冗余和特征聚合的计算开销，可以很容易地部署在便携式 GPU 上。第二，引入了一种基于预训练网络分割输出的简单后处理方案 max logit (ML) 来定位意外的道路障碍物。我们的“E-BiSeNet ＋ML”模型在现实世界和合成数据集上都优于最先进的方法。通过在户外场景中进行的各种实验，EOS的可行性和可靠性得到了广泛的验证。