基于轻量级FPNet的智能连接车辆对速度阻塞的视觉感知研究
Ruochen Wang1, Xiaoguo Luo1, Qing Ye2
1School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China.
Sensors (Basel, Switzerland)
|April 13, 2024
概括
这项研究介绍了YOLOv5-FPNet,这是一种轻量级的神经网络,用于在自动驾驶中准确和实时检测速度碰撞. 它增强了在具有挑战性的条件下更安全的航行感知.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 自主系统 自主系统
背景情况:
- 精确的速度碰撞识别对于自动驾驶安全至关重要.
- 现有的视觉感知算法在扭曲的图像和复杂的环境中面临着准确性和实时性能的挑战.
研究的目的:
- 提出一个增强的轻量级神经网络框架,YOLOv5-FPNet,用于准确和实时的速度碰撞检测.
- 提高对图像扭曲和复杂环境条件的感知算法的稳定性.
主要方法:
- 使用FasterNet和Dynamic Snake Convolution开发了FPNet,用于自适应的特征提取.
- 引入了C3-SFC模块,以提高部和头部部件的适应性.
- 集成的SimAM注意力机制用于关键特征提取.
- 设计了一个可适应的内部智能损失功能,以改进边界盒的装配.
主要成果:
- 与传统的轻量级网络相比,YOLOv5-FPNet在mAP (38.76%),mAP50_95 (143.15%) 和FPS (51.23%) 中显著改善.
- 废弃性研究验证了拟议增强的有效性.
- 在一个定制的减速器数据集上实现了卓越的性能.
结论:
- YOLOv5-FPNet提供了一个快速而准确的解决方案,用于自动驾驶汽车的速度碰撞检测.
- 该框架为智能汽车系统中的障碍物识别提供了宝贵的理论见解.
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