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针对现有辅助盲人出行导航系统检测精度不高、检测视野不全面的问题，设计了一种基于LPC-YOLO算法的全方位障碍物检测系统。本系统图像采集模块由分布在四个方向的摄像头组成，用于实时采集盲人行路四周的图像。提出了一种基于YOLOv8n的改进障碍物检测算法LPC-YOLO,若检测到障碍物，再使用单目测距算法进行障碍物测距。最后根据距离的不同，使用语音合成技术实时为盲人播报提示语音。LPC-YOLO算法改进了SPPF模块，减少了参数量并增强了特征提取能力；其次，引入了PPA注意力机制模块，使用不同大小的块进行多尺度特征提取，提升了小目标检测性能；最后，提出了一个CAFusion特征融合模块，提取低级特征与高级特征并进行特征融合，进一步增强特征提取能力。实验结果表明，在Obstacle-dataset上，改进的LPC-YOLO模型比原始的YOLOv8n模型平均精度均值mAP50提高了1.5百分点。本系统在室外道路晴好天气条件下的测试中，障碍物测距和语音提示方面也具有良好的表现，可为视障人士的出行提供有效的辅助。
Abstract：An omni-directional obstacle detection system based on the LPC-YOLO algorithm is developed to address the problems of low detection accuracy and incomplete detection field of view of the existing assisted blind travel navigation system. The image acquisition module of this system consists of cameras distributed in four directions, which are used to collect images around the blind travel path in real time. An improved obstacle detection algorithm LPC-YOLO based on YOLOv8n is proposed. If an obstacle is detected, the monocular ranging algorithm is then used for obstacle ranging. Finally, according to the distance difference, speech synthesis technology is used to broadcast prompt speech for the blind in real time. The LPC-YOLO algorithm improves the SPPF module to reduce the number of parameters and enhance the feature extraction capability. Secondly, a PPA attention mechanism module is introduced to use different sized blocks for multi-scale feature extraction, which improves the performance of small-target detection. Lastly, a CAFusion feature fusion module is proposed to extract the low-level features and high-level features and perform feature fusion to further enhance the feature extraction capability. The experimental results show that the improved LPC-YOLO model improves the mean accuracy(mAP50) by 1.5 percentage points over the original YOLOv8n model on the obstacle data set. The system also performs well in terms of obstacle detection and voice prompts in tests under sunny weather conditions on outdoor roads, which can provide effective travel assistance to the visually impaired.
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基本信息:
DOI：10.20165/j.cnki.ISSN1673-629X.2025.0236
中图分类号:TP391.41;TP18
引用信息:
[1]刘聪,房媛.基于深度学习的盲人行路全方位障碍物检测系统[J].计算机技术与发展,2026,36(02):62-70.DOI:10.20165/j.cnki.ISSN1673-629X.2025.0236.
基金信息:
辽宁省教育科研项目(JYTMS20230416); 辽宁省自然基金规划项目(2022-BS-263)
2025-08-19
2025-08-19
2025-08-19