在非结构化道路上,自动驾驶汽车使用优化双边神经网络在非结构化道路上识别驾驶区域
Xing Chen1, Yujiao Dong2, Xinyong Li3
1College of Intelligent Manufacturing Engineering, Chongqing University of Arts and Sciences, Yongchuan, 402160, Chongqing Province, P.R. China. bit_cx@163.com.
Scientific reports
|April 19, 2025
概括
本研究介绍了一种轻量级的语义细分网络,用于在自动驾驶汽车中准确识别可驾驶区域. 该模型通过有效处理复杂的边界并实现实时性能,提高了非结构化道路的安全性.
科学领域:
- 计算机视觉 计算机视觉
- 自主系统 自主系统
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 准确的可驾驶区域识别对于自动驾驶汽车的安全至关重要,尤其是在形状多样,边界模糊的非结构化道路上.
- 现有的模型往往无法在复杂的道路场景中平衡实时性能和高精度.
研究的目的:
- 提出一个轻量级的双边语义细分网络,具有双重注意力机制,用于增强可驾驶区域识别.
- 提高无结构道路上的自动驾驶系统的准确性和实时能力.
主要方法:
- 开发了一个轻量级的双边语义细分网络,在BiSeNet框架内集成高效通道注意力 (ECA) 和协调注意力 (CA) 机制.
- 整合了剩余网络以提高效率和全球卷积网络 (GCN) 与边界精制 (BR) 模块以提高细分精度.
- 使用双路径结构来捕获道和空间信息,解决不清楚的边界和复杂的背景.
主要成果:
- 在ORFD数据集上实现了93.89%的平均交叉点 (MIoU) 和97.32%的像素精度 (PA).
- 实时表现的实时性能以每秒62.49 (FPS) 的速度.
- 在准确性和速度上优于现有的先进实时语义细分模型.
结论:
- 拟议的模型为自动驾驶汽车所必需的实时,高精度的可驾驶区域识别提供了一个有希望的解决方案.
- 双重注意力机制和轻量级设计的整合确保了在动态,非结构化的环境中高效准确的性能.
- 这一进步有助于自动驾驶汽车的安全和高效运行.
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