障碍物
计算机科学
分割
人工智能
冗余(工程)
可靠性(半导体)
方案(数学)
市场细分
特征(语言学)
计算
避障
计算机视觉
人工神经网络
机器学习
实时计算
算法
政治学
法学
数学分析
功率(物理)
语言学
物理
哲学
数学
量子力学
营销
机器人
业务
移动机器人
操作系统
作者
Yinan Ma,Qi Xu,Yue Wang,Jing Wu,Chengnian Long,Yi‐Bing Lin
标识
DOI:10.1016/j.future.2022.09.017
摘要
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.
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