基于改进的YOLO11算法的车辆与道路合作自动驾驶的物体检测模型
Enqiang Liang1, Dongpo Wei2, Feng Li2
1Department of Mechanical Engineering, Shandong Huayu University of Technology, Dezhou, 253034, Shandong, China. enqiangliang@163.com.
Scientific reports
|September 2, 2025
概括
这项研究引入了一种改进的YOLOv11算法,用于自动驾驶,在复杂的闭塞场景中提高小目标检测的准确性. 这种新方法显著提高了DAIR-V2X-I数据集的性能.
科学领域:
- 计算机视觉
- 人工智能
- 自主系统
背景情况:
- 自动驾驶系统在复杂的路边和遥远场景中准确检测小,隐蔽的目标面临挑战.
- 现有的算法在低检测准确度,错误检测和错过检测方面面临困难.
研究的目的:
- 开发基于YOLOv11的自动驾驶小型目标检测算法.
- 在复杂场景中增强特征提取,小目标理解和模型效率.
主要方法:
- 嵌入式通道转移注意 (C3CTA模块) 在骨干网络中,以改善特征提取和阻塞处理.
- 在部引入扩散聚焦金字塔网络,以增强复杂场景中的小目标理解.
- 实施轻量级共享卷积检测头,以减少轻量级要求的模型参数.
主要成果:
- 在DAIR-V2X-I数据集中,改进的算法获得了85.7%的精度,79.4%的回忆率,85.3%的mAP@0.5和61.3%的mAP@0.5-95.
- 与基线模型相比显著改善,主要指标增长了4. 0%,3.1%,2. 4%和2. 8%.
- 成功解决重叠封闭的挑战,并改善了车辆目标特征的提取.
结论:
- 拟议的基于YOLOv11的算法有效地提高了自动驾驶中小目标检测的准确性.
- 整合C3CTA,扩散聚焦金字塔网络和轻量级共享卷积检测头证明是有效的.
- 这项工作为在具有挑战性的自动驾驶环境中强大的目标检测提供了一个新的解决方案.
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