基于PP-YOLOE的EdgeBoard智能车辆的优化设计
Chengzhang Yao1, Xiangpeng Liu1, Jilin Wang2
1College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai 201418, China.
Sensors (Basel, Switzerland)
|May 25, 2024
概括
我们为智能汽车开发了一个优化的PP-YOLOE+模型,提高了检测准确性和效率. 这种先进的系统在紧的模型尺寸下实现了高性能,提高了自动驾驶能力.
科学领域:
- 计算机视觉 计算机视觉
- 深度学习 (Deep Learning) 是一种深度学习.
- 自主智能汽车自动驾驶汽车
背景情况:
- 深度学习和计算机视觉已经推进了自动驾驶汽车技术.
- 提高EdgeBoard智能车辆的检测精度和效率至关重要.
研究的目的:
- 使用PP-YOLOE+模型提出一个优化的EdgeBoard设计.
- 为智能汽车增强环境感知和传感器数据分析.
主要方法:
- 开发了一个复合骨干网络 (深度残余网络,特征金字塔网络,RepResBlock).
- 包含一个高效的任务调整头 (ET-head),用于精确的传感器解释.
- 设计定制硬件 (主板,驱动板) 并优化了Pos-PID控制算法.
- 利用边缘计算,动态标签赋值和4777个不同图像的自定义数据集.
主要成果:
- 实现了99.113%的准确性,54.9%的mAP和192 FPS的检测率.
- 模型尺寸是紧的81 MB,证明了效率.
- 与现有的算法相比,在检测准确度和计算速度之间取得了卓越的平衡.
结论:
- PP-YOLOE+型号为智能汽车提供了卓越的性能.
- 优化的设计提高了安全性,可靠性和实时处理.
- 这项工作代表了自动驾驶感知系统的重大进步.
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