Diwei Sheng,Giles Hamilton-Fletcher,Mahya Beheshti et al.
Diwei Sheng et al.
Herein, we investigate the efficacy of curb segmentation for foundation models. We introduce the largest curb segmentation dataset to date to benchmark leading foundation models. Our results show that state-of-the-art foundation models face significant challenges in curb segmentation.
Multi-faceted sensory substitution using wearable technology for curb alerting: a pilot investigation with persons with blindness and low vision [0.03%]
利用可穿戴技术进行多方面的感官替代以提醒路沿：对盲人和低视力者的初步调查
Ligao Ruan,Giles Hamilton-Fletcher,Mahya Beheshti et al.
Ligao Ruan et al.
Future enhancements will focus on expanding our curb segmentation dataset, improving distance estimations through advanced 3D sensors and AI-models, refining system calibration and stability, and developing user-centric sonification methods to cater for a diverse range of visual impairments.
Multi-Feature-Filtering-Based Road Curb Extraction from Unordered Point Clouds [0.03%]
基于多特征过滤的无序点云道路边沿提取算法
Hong Lang,Yuan Peng,Zheng Zou et al.
Hong Lang et al.
Specifically, the average curb segmentation precision, recall, and F1 score values across scenarios A, B (intersections), C (straight road), and scenarios D and E (curved roads and ghosting) are 0.9365, 0.782, and 0.8523, respectively.