LEAD-YOLO:一种轻量级且准确的网络,用于自动驾驶中检测小物体
Yunchuan Yang1, Shubin Yang1, Qiqing Chan1
1School of Electrical and Information Engineering, Wuhan Institute of Technology, Wuhan 430205, China.
Sensors (Basel, Switzerland)
|August 14, 2025
概括
LEAD-YOLO增强了用于自动驾驶的小型物体检测. 这种轻量级网络提高了准确性和效率,这对于车辆的实时边缘部署至关重要.
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:
- 精确的小物体检测对于自动驾驶安全至关重要.
- 现有的方法往往会增加模型的复杂性,阻碍边缘部署.
- 平衡检测性能和计算效率是一个关键的挑战.
研究的目的:
- 提出LEAD-YOLO,一个轻量级和高效的网络用于自动驾驶中检测小物体.
- 为了提高小物体的感知和上下文的理解,而不损害效率.
- 为了实现卓越的检测准确性,同时保持边缘设备的计算效率.
主要方法:
- LEAD-YOLO集成了一个卷积门式变压器 (CGF) 和扩展特征融合 (DFF) 在骨干中.
- 在子中使用层次特征融合模块 (HFFM) 进行指导特征聚合.
- 在头部中使用了一个共享的特征检测头 (SFDH),具有共享的模块和细节增强分支.
主要成果:
- LEAD-YOLO实现了3.8%和5.4%的mAP优化,对数值图像进行了改进,参数减少了24.1%.
- 在VisDrone2019上,mAP@0.5和mAP@[0.5:0.95]的性能增长分别达到7.9%和6.4%.
- 在检测精度和模型效率之间显示出显著的平衡.
结论:
- LEAD-YOLO有效地解决了自动驾驶中小物体检测的挑战.
- 拟议的架构为高效的边缘部署提供了一个引人注目的解决方案.
- LEAD-YOLO显示了现实世界自动驾驶应用的巨大潜力.
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