计算机与现代化Issue(1)：30-39,10.DOI:10.3969/j.issn.1006-2475.2026.01.005
盲道识别与障碍物检测的多任务模型
Multi-task Models for Blind Lane Recognition and Obstacle Detection
徐浩闻 1李维乾1
作者信息
- 1. 西安工程大学计算机科学学院,陕西 西安 710600
- 折叠
摘要
Abstract
This paper proposes the Amaterasu-YOLO multi-task model aimed at improving the accuracy and efficiency of blind path area segmentation and obstacle detection.The model integrates an Adaptive Cascade Module(ECD)and a Multi-Receptive Spatial Attention Module(MRSA),enabling high-precision blind path segmentation and obstacle detection in complex urban en-vironments.By leveraging multi-task learning,Amaterasu-YOLO not only optimizes the joint tasks of blind path segmentation and obstacle detection but also significantly reduces computational burden,enhancing the model's efficiency on resource-constrained edge devices.Experimental results show that Amaterasu-YOLO achieves good performance in both blind path seg-mentation and obstacle detection tasks,with segmentation accuracy reaching 90%and obstacle detection accuracy reaching 85%.Compared to traditional single-task methods,the model demonstrates stronger robustness and practicality,with broad ap-plication potential in smart city development and ensuring the safety of visually impaired individuals.关键词
YOLOv8/盲道分割/障碍物检测/多任务模型/注意力机制/目标检测Key words
YOLOv8/blind road segmentation/obstacle detection/multi-task model/attention mechanisms/object detection分类
信息技术与安全科学引用本文复制引用
徐浩闻,李维乾..盲道识别与障碍物检测的多任务模型[J].计算机与现代化,2026,(1):30-39,10.
