基于多尺度特征的智能车辆目标检测算法
Aijuan Li1, Xiangsen Ning1, Máté Zöldy2
1School of Automotive Engineering, Shandong Jiaotong University, Jinan 250357, China.
Sensors (Basel, Switzerland)
|August 28, 2025
概括
这项研究优化了YOLOv10对象检测模型的智能驾驶,将其尺寸减少了11.8%,同时实现了93.0%的准确性. 改进的模型为自动驾驶系统提供了更好的检测性能.
科学领域:
- 计算机视觉
- 人工智能
- 智能交通系统
背景情况:
- 在复杂的智能驾驶场景中,对象检测模型经常面临错误和错误检测的挑战.
- 现有的模型可能具有很高的计算负载和大尺寸,限制了它们的实时应用.
研究的目的:
- 优化YOLOv10算法以提高对象检测准确性和减少智能驾驶中的模型复杂性.
- 开发一个更高效,更有效的自动驾驶汽车检测框架.
主要方法:
- 设计多尺度灵活卷积 (MSFC) 来捕获同时多尺度的信息,减少网络深度和计算成本.
- 使用浅辅助融合 (SAF) 和高级辅助融合 (AAF) 重建了子网络,以改善多尺度特征提取.
- 增强了检测头部的多尺度卷积和通道适应性注意力机制,用于多样化和准确的特征提取.
主要成果:
- 优化的YOLOv10模型实现了13.4 MB的文件大小,与原始模型相比减少了11.8%.
- 该模型的平均精度 (mAP@0.5) 达到了93.0%,显示出卓越的检测精度.
- 这种改进的模型在整体性能,精度和尺寸方面表现优于主流的物体检测模型.
结论:
- 拟议的改进显著提高了YOLOv10在智能驾驶应用中的性能.
- 这种优化模型为实时物体检测提供了一个实用的解决方案, 通过减少计算资源来平衡高精度.
- 开发的框架为智能驾驶场景提供了强大的检测系统.
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